{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prime Editing (PE) Coverage Analysis – NGG CORRECTED VERSION\n",
    "\n",
    "- This notebook provides a quantification of the capabilities of prime editing to model mutations present in the MSK IMPACT dataset.\n",
    "- Assumptions:\n",
    "    - Using an NGG PAM site for the base editor\n",
    "    - Haven't thoroughly analyzed potential off-target effects of a given pegRNA\n",
    "        - Focusing on simply ability to target a mutationw with a pegRNA\n",
    "\n",
    "For more information regarding MSK IMPACT dataset collection methods, etc., see: \n",
    "- https://www.mskcc.org/msk-impact\n",
    "- https://datacatalog.mskcc.org/dataset/10438"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/5g/xxjcy2g531n02hnyb6d8qdsr0000gn/T/ipykernel_25055/836307472.py:2: DtypeWarning: Columns (45,48,88) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  impact_data = pd.read_csv(filepath, sep='\\t')\n"
     ]
    }
   ],
   "source": [
    "filepath = '/Volumes/Sam_G_SSD/2020-06-16-MSK-IMPACT_EDITED.txt'\n",
    "impact_data = pd.read_csv(filepath, sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th>Entrez_Gene_Id</th>\n",
       "      <th>Center</th>\n",
       "      <th>NCBI_Build</th>\n",
       "      <th>Chromosome</th>\n",
       "      <th>Start_Position</th>\n",
       "      <th>End_Position</th>\n",
       "      <th>Strand</th>\n",
       "      <th>Consequence</th>\n",
       "      <th>Variant_Classification</th>\n",
       "      <th>...</th>\n",
       "      <th>VARIANT_CLASS</th>\n",
       "      <th>all_effects</th>\n",
       "      <th>amino_acid_change</th>\n",
       "      <th>cDNA_Change</th>\n",
       "      <th>cDNA_position</th>\n",
       "      <th>cdna_change</th>\n",
       "      <th>comments</th>\n",
       "      <th>n_depth</th>\n",
       "      <th>t_depth</th>\n",
       "      <th>transcript</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BRCA2</td>\n",
       "      <td>675</td>\n",
       "      <td>MSKCC</td>\n",
       "      <td>GRCh37</td>\n",
       "      <td>13</td>\n",
       "      <td>32937315</td>\n",
       "      <td>32937315</td>\n",
       "      <td>+</td>\n",
       "      <td>splice_acceptor_variant</td>\n",
       "      <td>Splice_Site</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BRCA2</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC</td>\n",
       "      <td>37</td>\n",
       "      <td>13</td>\n",
       "      <td>32914437</td>\n",
       "      <td>32914438</td>\n",
       "      <td>+</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MUTYH</td>\n",
       "      <td>4595</td>\n",
       "      <td>MSKCC</td>\n",
       "      <td>GRCh37</td>\n",
       "      <td>1</td>\n",
       "      <td>45798475</td>\n",
       "      <td>45798475</td>\n",
       "      <td>+</td>\n",
       "      <td>missense_variant</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BRCA2</td>\n",
       "      <td>675</td>\n",
       "      <td>MSKCC</td>\n",
       "      <td>GRCh37</td>\n",
       "      <td>13</td>\n",
       "      <td>32893302</td>\n",
       "      <td>32893302</td>\n",
       "      <td>+</td>\n",
       "      <td>frameshift_variant</td>\n",
       "      <td>Frame_Shift_Ins</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BRCA1</td>\n",
       "      <td>0</td>\n",
       "      <td>MSKCC</td>\n",
       "      <td>37</td>\n",
       "      <td>17</td>\n",
       "      <td>41251824</td>\n",
       "      <td>41251825</td>\n",
       "      <td>+</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422817</th>\n",
       "      <td>SMARCA4</td>\n",
       "      <td>6597</td>\n",
       "      <td>MSKCC</td>\n",
       "      <td>GRCh37</td>\n",
       "      <td>19</td>\n",
       "      <td>11144132</td>\n",
       "      <td>11144132</td>\n",
       "      <td>+</td>\n",
       "      <td>missense_variant</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422818</th>\n",
       "      <td>BRAF</td>\n",
       "      <td>673</td>\n",
       "      <td>MSKCC</td>\n",
       "      <td>GRCh37</td>\n",
       "      <td>7</td>\n",
       "      <td>140453149</td>\n",
       "      <td>140453149</td>\n",
       "      <td>+</td>\n",
       "      <td>missense_variant</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422819</th>\n",
       "      <td>NRAS</td>\n",
       "      <td>4893</td>\n",
       "      <td>MSKCC</td>\n",
       "      <td>GRCh37</td>\n",
       "      <td>1</td>\n",
       "      <td>115258747</td>\n",
       "      <td>115258747</td>\n",
       "      <td>+</td>\n",
       "      <td>missense_variant</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422820</th>\n",
       "      <td>TERT</td>\n",
       "      <td>7015</td>\n",
       "      <td>MSKCC</td>\n",
       "      <td>GRCh37</td>\n",
       "      <td>5</td>\n",
       "      <td>1295521</td>\n",
       "      <td>1295521</td>\n",
       "      <td>+</td>\n",
       "      <td>upstream_gene_variant</td>\n",
       "      <td>5'Flank</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422821</th>\n",
       "      <td>KRAS</td>\n",
       "      <td>3845</td>\n",
       "      <td>MSKCC</td>\n",
       "      <td>GRCh37</td>\n",
       "      <td>12</td>\n",
       "      <td>25398284</td>\n",
       "      <td>25398284</td>\n",
       "      <td>+</td>\n",
       "      <td>missense_variant</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>422822 rows × 123 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Hugo_Symbol  Entrez_Gene_Id Center NCBI_Build Chromosome  \\\n",
       "0            BRCA2             675  MSKCC     GRCh37         13   \n",
       "1            BRCA2               0  MSKCC         37         13   \n",
       "2            MUTYH            4595  MSKCC     GRCh37          1   \n",
       "3            BRCA2             675  MSKCC     GRCh37         13   \n",
       "4            BRCA1               0  MSKCC         37         17   \n",
       "...            ...             ...    ...        ...        ...   \n",
       "422817     SMARCA4            6597  MSKCC     GRCh37         19   \n",
       "422818        BRAF             673  MSKCC     GRCh37          7   \n",
       "422819        NRAS            4893  MSKCC     GRCh37          1   \n",
       "422820        TERT            7015  MSKCC     GRCh37          5   \n",
       "422821        KRAS            3845  MSKCC     GRCh37         12   \n",
       "\n",
       "        Start_Position  End_Position Strand              Consequence  \\\n",
       "0             32937315      32937315      +  splice_acceptor_variant   \n",
       "1             32914437      32914438      +                      NaN   \n",
       "2             45798475      45798475      +         missense_variant   \n",
       "3             32893302      32893302      +       frameshift_variant   \n",
       "4             41251824      41251825      +                      NaN   \n",
       "...                ...           ...    ...                      ...   \n",
       "422817        11144132      11144132      +         missense_variant   \n",
       "422818       140453149     140453149      +         missense_variant   \n",
       "422819       115258747     115258747      +         missense_variant   \n",
       "422820         1295521       1295521      +    upstream_gene_variant   \n",
       "422821        25398284      25398284      +         missense_variant   \n",
       "\n",
       "       Variant_Classification  ... VARIANT_CLASS all_effects  \\\n",
       "0                 Splice_Site  ...           NaN         NaN   \n",
       "1                         NaN  ...           NaN         NaN   \n",
       "2           Missense_Mutation  ...           NaN         NaN   \n",
       "3             Frame_Shift_Ins  ...           NaN         NaN   \n",
       "4                         NaN  ...           NaN         NaN   \n",
       "...                       ...  ...           ...         ...   \n",
       "422817      Missense_Mutation  ...           NaN         NaN   \n",
       "422818      Missense_Mutation  ...           NaN         NaN   \n",
       "422819      Missense_Mutation  ...           NaN         NaN   \n",
       "422820                5'Flank  ...           NaN         NaN   \n",
       "422821      Missense_Mutation  ...           NaN         NaN   \n",
       "\n",
       "       amino_acid_change cDNA_Change cDNA_position  cdna_change comments  \\\n",
       "0                    NaN         NaN           NaN          NaN      NaN   \n",
       "1                    NaN         NaN           NaN          NaN      NaN   \n",
       "2                    NaN         NaN           NaN          NaN      NaN   \n",
       "3                    NaN         NaN           NaN          NaN      NaN   \n",
       "4                    NaN         NaN           NaN          NaN      NaN   \n",
       "...                  ...         ...           ...          ...      ...   \n",
       "422817               NaN         NaN           NaN          NaN      NaN   \n",
       "422818               NaN         NaN           NaN          NaN      NaN   \n",
       "422819               NaN         NaN           NaN          NaN      NaN   \n",
       "422820               NaN         NaN           NaN          NaN      NaN   \n",
       "422821               NaN         NaN           NaN          NaN      NaN   \n",
       "\n",
       "        n_depth  t_depth  transcript  \n",
       "0           NaN      NaN         NaN  \n",
       "1           NaN      NaN         NaN  \n",
       "2           NaN      NaN         NaN  \n",
       "3           NaN      NaN         NaN  \n",
       "4           NaN      NaN         NaN  \n",
       "...         ...      ...         ...  \n",
       "422817      NaN      NaN         NaN  \n",
       "422818      NaN      NaN         NaN  \n",
       "422819      NaN      NaN         NaN  \n",
       "422820      NaN      NaN         NaN  \n",
       "422821      NaN      NaN         NaN  \n",
       "\n",
       "[422822 rows x 123 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "impact_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 594 unique genes observed\n"
     ]
    }
   ],
   "source": [
    "#list of unique genes tested\n",
    "unique_genes = np.unique(np.asarray(impact_data['Hugo_Symbol']))\n",
    "print('There are ' + str(len(unique_genes)) + ' unique genes observed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading in reference human genome for epegRNA design\n",
    "\n",
    "- can check for PAM sequences on either side of the mutation (+/- strand?)\n",
    "- Downloading reference sequence from: https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.26/ (assembly GRCh38)\n",
    "- GRCh37: https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.25\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Bio import SeqIO\n",
    "import gzip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['37', 'GRCh37'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#MSK IMPACT dataset uses GrCh37 build\n",
    "np.unique(np.asarray(impact_data['NCBI_Build']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = '/Volumes/Sam_G_SSD/GRCh37/ncbi-genomes-2022-03-17/GCF_000001405.25_GRCh37.p13_genomic.fna.gz'\n",
    "\n",
    "with gzip.open(file, \"rt\") as handle:\n",
    "    records = list(SeqIO.parse(handle, \"fasta\")) #about 4 Gb in  memory\n",
    "    #records = list that contains sequences split up by chromosome (and intrachromosome splits up to some size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['NC_000001.10 Homo sapiens chromosome 1, GRCh37.p13 Primary Assembly',\n",
       " 'NC_000002.11 Homo sapiens chromosome 2, GRCh37.p13 Primary Assembly',\n",
       " 'NC_000003.11 Homo sapiens chromosome 3, GRCh37.p13 Primary Assembly',\n",
       " 'NC_000004.11 Homo sapiens chromosome 4, GRCh37.p13 Primary Assembly',\n",
       " 'NC_000005.9 Homo sapiens chromosome 5, GRCh37.p13 Primary Assembly',\n",
       " 'NC_000006.11 Homo sapiens chromosome 6, GRCh37.p13 Primary Assembly',\n",
       " 'NC_000007.13 Homo sapiens chromosome 7, GRCh37.p13 Primary Assembly',\n",
       " 'NC_000008.10 Homo sapiens chromosome 8, GRCh37.p13 Primary Assembly',\n",
       " 'NC_000009.11 Homo sapiens chromosome 9, GRCh37.p13 Primary Assembly',\n",
       " 'NC_000010.10 Homo sapiens chromosome 10, GRCh37.p13 Primary Assembly',\n",
       " 'NC_000011.9 Homo sapiens chromosome 11, GRCh37.p13 Primary Assembly',\n",
       " 'NC_000012.11 Homo sapiens chromosome 12, GRCh37.p13 Primary Assembly',\n",
       " 'NC_000013.10 Homo sapiens chromosome 13, GRCh37.p13 Primary Assembly',\n",
       " 'NC_000014.8 Homo sapiens chromosome 14, GRCh37.p13 Primary Assembly',\n",
       " 'NC_000015.9 Homo sapiens chromosome 15, GRCh37.p13 Primary Assembly',\n",
       " 'NC_000016.9 Homo sapiens chromosome 16, GRCh37.p13 Primary Assembly',\n",
       " 'NC_000017.10 Homo sapiens chromosome 17, GRCh37.p13 Primary Assembly',\n",
       " 'NC_000018.9 Homo sapiens chromosome 18, GRCh37.p13 Primary Assembly',\n",
       " 'NC_000019.9 Homo sapiens chromosome 19, GRCh37.p13 Primary Assembly',\n",
       " 'NC_000020.10 Homo sapiens chromosome 20, GRCh37.p13 Primary Assembly',\n",
       " 'NC_000021.8 Homo sapiens chromosome 21, GRCh37.p13 Primary Assembly',\n",
       " 'NC_000022.10 Homo sapiens chromosome 22, GRCh37.p13 Primary Assembly',\n",
       " 'NC_000023.10 Homo sapiens chromosome X, GRCh37.p13 Primary Assembly',\n",
       " 'NC_000024.9 Homo sapiens chromosome Y, GRCh37.p13 Primary Assembly',\n",
       " 'NC_012920.1 Homo sapiens mitochondrion, complete genome']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filtering out alternative sequences to only select consensus matches\n",
    "\n",
    "\n",
    "wrong = [\"alternate\", \"unplaced\", \"unlocalized\", \"patch\"]\n",
    "badlist = []\n",
    "for key in wrong:\n",
    "    for i in records:\n",
    "        ii = i.description\n",
    "        if key in ii:\n",
    "            badlist.append(ii)\n",
    "            \n",
    "filtered = []\n",
    "index_list = []\n",
    "for idx, i in enumerate(records):\n",
    "    ii = i.description\n",
    "    if ii not in badlist:\n",
    "        filtered.append(ii)\n",
    "        index_list.append(idx)\n",
    "        \n",
    "filtered\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SeqRecord(seq=Seq('NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN...NNN'), id='NC_000020.10', name='NC_000020.10', description='NC_000020.10 Homo sapiens chromosome 20, GRCh37.p13 Primary Assembly', dbxrefs=[])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chromosome = 20\n",
    "#22 (23-1) = X\n",
    "#23 (24-1) = Y\n",
    "#24 (25-1) = mitochondrial DNA\n",
    "records[index_list[chromosome-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ref = TCATCG\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Seq('TCATCG')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k=902\n",
    "print('ref = ' + impact_data.iloc[[k]]['Reference_Allele'].values[0])\n",
    "#print('al1 = ' + impact_data.iloc[[k]]['Tumor_Seq_Allele1'].values[0])\n",
    "#print('al2 = ' + impact_data.iloc[[k]]['Tumor_Seq_Allele2'].values[0])\n",
    "seq_start = impact_data.iloc[[k]]['Start_Position'].values[0]\n",
    "seq_end = impact_data.iloc[[k]]['End_Position'].values[0]\n",
    "chromosome = impact_data.iloc[[k]]['Chromosome'].values[0]\n",
    "seq1 = records[index_list[int(chromosome)-1]].seq\n",
    "seq1[seq_start-1: seq_end]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sequence start is offset by 1 (i.e. need to subtract 1 from start position to get true sequence information). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading in gene coordinate information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>transcript_id</th>\n",
       "      <th>chrom</th>\n",
       "      <th>gene_start</th>\n",
       "      <th>gene_end</th>\n",
       "      <th>transcript_start</th>\n",
       "      <th>transcript_end</th>\n",
       "      <th>strand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABL1</td>\n",
       "      <td>ENSG00000097007.13</td>\n",
       "      <td>ENST00000318560.5</td>\n",
       "      <td>chr9</td>\n",
       "      <td>133589333</td>\n",
       "      <td>133763062</td>\n",
       "      <td>133710453</td>\n",
       "      <td>133763062</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AC004906.3</td>\n",
       "      <td>ENSG00000237286.1</td>\n",
       "      <td>ENST00000423194.1</td>\n",
       "      <td>chr7</td>\n",
       "      <td>2983669</td>\n",
       "      <td>2986725</td>\n",
       "      <td>2983669</td>\n",
       "      <td>2986725</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AC008738.1</td>\n",
       "      <td>ENSG00000230259.2</td>\n",
       "      <td>ENST00000425420.2</td>\n",
       "      <td>chr19</td>\n",
       "      <td>33790853</td>\n",
       "      <td>33793430</td>\n",
       "      <td>33790853</td>\n",
       "      <td>33793430</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACTG1</td>\n",
       "      <td>ENSG00000184009.5</td>\n",
       "      <td>ENST00000575842.1</td>\n",
       "      <td>chr17</td>\n",
       "      <td>79476997</td>\n",
       "      <td>79490873</td>\n",
       "      <td>79477015</td>\n",
       "      <td>79479807</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACVR1</td>\n",
       "      <td>ENSG00000115170.9</td>\n",
       "      <td>ENST00000263640.3</td>\n",
       "      <td>chr2</td>\n",
       "      <td>158592958</td>\n",
       "      <td>158732374</td>\n",
       "      <td>158592958</td>\n",
       "      <td>158731623</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>XRCC2</td>\n",
       "      <td>ENSG00000196584.2</td>\n",
       "      <td>ENST00000359321.1</td>\n",
       "      <td>chr7</td>\n",
       "      <td>152341864</td>\n",
       "      <td>152373250</td>\n",
       "      <td>152343589</td>\n",
       "      <td>152373250</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>YAP1</td>\n",
       "      <td>ENSG00000137693.9</td>\n",
       "      <td>ENST00000282441.5</td>\n",
       "      <td>chr11</td>\n",
       "      <td>101981192</td>\n",
       "      <td>102104154</td>\n",
       "      <td>101981192</td>\n",
       "      <td>102104154</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>YES1</td>\n",
       "      <td>ENSG00000176105.9</td>\n",
       "      <td>ENST00000314574.4</td>\n",
       "      <td>chr18</td>\n",
       "      <td>721588</td>\n",
       "      <td>812547</td>\n",
       "      <td>721748</td>\n",
       "      <td>812239</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>ZFHX3</td>\n",
       "      <td>ENSG00000140836.10</td>\n",
       "      <td>ENST00000268489.5</td>\n",
       "      <td>chr16</td>\n",
       "      <td>72816784</td>\n",
       "      <td>73093597</td>\n",
       "      <td>72816784</td>\n",
       "      <td>73082274</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>ZRSR2</td>\n",
       "      <td>ENSG00000169249.8</td>\n",
       "      <td>ENST00000307771.7</td>\n",
       "      <td>chrX</td>\n",
       "      <td>15808595</td>\n",
       "      <td>15841383</td>\n",
       "      <td>15808595</td>\n",
       "      <td>15841383</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>594 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           gene             gene_id      transcript_id  chrom  gene_start  \\\n",
       "0          ABL1  ENSG00000097007.13  ENST00000318560.5   chr9   133589333   \n",
       "1    AC004906.3   ENSG00000237286.1  ENST00000423194.1   chr7     2983669   \n",
       "2    AC008738.1   ENSG00000230259.2  ENST00000425420.2  chr19    33790853   \n",
       "3         ACTG1   ENSG00000184009.5  ENST00000575842.1  chr17    79476997   \n",
       "4         ACVR1   ENSG00000115170.9  ENST00000263640.3   chr2   158592958   \n",
       "..          ...                 ...                ...    ...         ...   \n",
       "589       XRCC2   ENSG00000196584.2  ENST00000359321.1   chr7   152341864   \n",
       "590        YAP1   ENSG00000137693.9  ENST00000282441.5  chr11   101981192   \n",
       "591        YES1   ENSG00000176105.9  ENST00000314574.4  chr18      721588   \n",
       "592       ZFHX3  ENSG00000140836.10  ENST00000268489.5  chr16    72816784   \n",
       "593       ZRSR2   ENSG00000169249.8  ENST00000307771.7   chrX    15808595   \n",
       "\n",
       "      gene_end  transcript_start  transcript_end strand  \n",
       "0    133763062         133710453       133763062      +  \n",
       "1      2986725           2983669         2986725      +  \n",
       "2     33793430          33790853        33793430      -  \n",
       "3     79490873          79477015        79479807      -  \n",
       "4    158732374         158592958       158731623      -  \n",
       "..         ...               ...             ...    ...  \n",
       "589  152373250         152343589       152373250      -  \n",
       "590  102104154         101981192       102104154      +  \n",
       "591     812547            721748          812239      -  \n",
       "592   73093597          72816784        73082274      -  \n",
       "593   15841383          15808595        15841383      +  \n",
       "\n",
       "[594 rows x 9 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename1 = '/Users/samgould/Desktop/FSR Lab/2022-03-17/gene_info.csv'\n",
    "df1 = pd.read_csv(filename1)\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prime Editing Coverage Quantification\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assumptions\n",
    "- For insertions and deletions, also need to take into account the size of the indel...\n",
    "- using an NGG PAM sequence\n",
    "- when looking at the size of the RT template, not taking into account the need for homology at the end of the template...\n",
    "    - this can be accounted for easily by reducing the size of the RT template."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "def prime_domains(gene_name, rt_template_length, df1):\n",
    "    \"Returns vector containing pegRNA coverage for a given gene (+/- 1000 bp of start/end)\"\n",
    "    \n",
    "    #df1 is the dataframe containing gene sequence information\n",
    "\n",
    "    gene_chrom = df1[df1['gene']==gene_name]['chrom'].values[0]\n",
    "    \n",
    "    if gene_chrom != 'chrX':\n",
    "        chrom = int(re.search(r'\\d+', gene_chrom).group())\n",
    "    elif gene_chrom=='chrX':\n",
    "        chrom = 23 #it's number 22, but we subtract 1 below when accessing chromosome file\n",
    "        #there are no genes in this dataset that fall on Y chromosome -- ignoring\n",
    "\n",
    "    seq_start = df1[df1['gene']==gene_name]['gene_start'].values[0]\n",
    "    seq_end = df1[df1['gene']==gene_name]['gene_end'].values[0]\n",
    "\n",
    "    gene_sequence_1000eitherend = records[index_list[int(chrom)-1]].seq[seq_start-1001: seq_end+1000]\n",
    "    #gene sequence with 1000 bp buffer on either end to account for prime domains at beginning of gene\n",
    "    #shitty variable naming; please forgive\n",
    "\n",
    "    length_seq = len(gene_sequence_1000eitherend)\n",
    "\n",
    "    zero_array = np.zeros(length_seq)\n",
    "\n",
    "    #iterate through sequence\n",
    "\n",
    "    #stop before the end to avoid weird boundary effects...\n",
    "    for idx, val in enumerate(gene_sequence_1000eitherend[:length_seq-50]): \n",
    "        \n",
    "        by_twos = gene_sequence_1000eitherend[idx:idx+2]\n",
    "        by_twos_true = by_twos.upper()\n",
    "        \n",
    "        if by_twos_true == 'GG': #find NGGs\n",
    "            zero_array[idx-4:idx+rt_template_length-4] += 1 \n",
    "            #add array of ones at appropriate index when NGG is encountered\n",
    "        \n",
    "        else:\n",
    "            continue   \n",
    "            \n",
    "\n",
    "    #repeat on negative strand\n",
    "    comp = gene_sequence_1000eitherend.complement() #taking complement (- strand in same orientation)\n",
    "    for idx, val in enumerate(comp):\n",
    "        \n",
    "        by_twos = comp[idx:idx+2]\n",
    "        by_twos_true = by_twos.upper()\n",
    "        \n",
    "        if by_twos_true == 'GG': #find NGGs\n",
    "            zero_array[idx+5-rt_template_length:idx+5] += 1#add array of ones at appropriate index when NGG is encountered\n",
    "            #+5 to account for counting from back G instead of front G in NGG\n",
    "        \n",
    "        else:\n",
    "            continue   \n",
    "    \n",
    "    \n",
    "    return zero_array\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename1 = '/Users/samgould/Desktop/FSR Lab/2022-03-17/gene_info.csv'\n",
    "df1 = pd.read_csv(filename1)\n",
    "\n",
    "gene_name = 'TP53'\n",
    "rt_template_length = 7\n",
    "\n",
    "kkk = prime_domains(gene_name, rt_template_length, df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rPRxKZ9h3KDs6fqolrLyvtD+U23+GMhnWrljI0vYW/m9bP0FgBOZ0trWQzXnFdVo8r5g5A8M5MqXRVQUG47aDgsVtcRYkE2PS3fDte9n4k23EAiMRQCZaF+tf1D1jTiyleRzKOHmgJcpv6fK2JozAbFqOXcXb53Amy88fexoHXnjcYnY8PcTPH3+aro5WlrW3jNvnajkG1/s8UC1NYErnhE/f/hAf+e6D5PLhn4C8h+uoMxXng793ctl8ltuvv7dlBzsGRjAgZpCL5nPBc7pYuiBZdbuttj/2rFla07FjJu0bZnZ3ucokNSk2yMZN22hPxulMJQiCgM5UgvZknI2bttVt/i2xGO3JGEEQ0J6M0RKL1W3+M0k9yrJ4HuWCLQgPDrkKvxXL5p17+gboTCV4Yt8Q2ZxPKm/X9vZhNn54Jg/ZnLNx07Zxy3wwnSMWGAPD2bLpFMbPRP8s25MxMONAOsPC1jiP7jlEZyrBvsER9h5Mh/M1ozOVoDOV4OYtu6qWXUsshkU1Msl4QDwWcP+TByec9uYtu0bTqDW9Snkotw1c29tHezLOgeEcgUEiZsQCyOS86v5Qbv+BgEf3DvLQnkFa4uGwRCzGSDZfdZ0Wz2sk57TEx67cagfZ0uDDCE96AP1D2XHpXtvbRywI10EQBCTjAbHAuLa3r3oBNlFpHgtG8oeXrcCAkWz1ddVIxdvno3sPsaA1Prq/PLRnkJZo/Vfa5yY6Btf7PFAtzameEzZu2kbewyApFli4DxkcHM5WnLbcfv3kwAgQ7oNBNB8Dbrx394TbbbX9sdZjx2zYNyYVcJnZYjN7iZm90cwWR8NazWwy83Hge2Z2t5ldUiGdS8ys18x69+zZM5kszhi7BoZZmIyNGbYwGWPXwHBd5h82XYw9eqUSRv9Qpi7zn0nqUZbl5lFObsIxwPMwmA5rU4azOfI+NkqbKG/DmXzFHS/veXYNDI/L70jOw39tRRFhcTqF8dPZPLGis1o2lycZDxgYDreLkWyedHbsUi5sjdO3f6hifgvbWi5sGQPC2rh0Nj/htH37h1jYOrbnwkTTlFNpGxjO5FmYjJF1H82bmZHPe9X9odz+E1hYPoPpHIlY+FsssHA5q6zT4nkVymjMnMsE1xMJCGsaStMdzuRJlGw8iSAcPlOU5rE4qCwtCiNczuk6dhVvnweHsyTjwej+MpjOkUqE67+geH3Ucgyu93mgWppTPSf0D2XGrZcgqqGqlM9y+3W5LTBm4R/UibbbavtjsWrHjtmwb9QUKJlZzMw+DGwHbgf+Czg2+vkbwPsnkebp7v584FzgrWZ2RukI7n61u/e4e8/y5csnMeuZo6ujlQPpsSe2A+kcXR2tdZl/ZyrBUGbs/+OhTFjTMtfUoyzLzaOciUMysADak+HBpjUeIyj5vzFR3loTQdmDE0BgAV0drePy2xIzMnlIxA6nVZxOYfxkPCBX1JwVjwWks3k6WsPtoiUekIyPXcoDw1lWLUpVzG9hW4vZ4ZNnJg/JeDDhtKsWpTgwnB0zbKJpyqm0DbQmAg6kc8TNRvPmHvbjqrY/lNt/8s5ozVYmF/6Wy3u4nFXWafG8CmU0Zs5T6LWRJzzhlKbbmgjG9S/M5MPhM0VpHotPo6VF4YTLOV3HruLtc0FrnHQ2P7q/tCdjDGXC9V9QvD5qOQbX+zxQLc2pnhM6U4lx66VQ41Upn+X263JbYM4hHtiE2221/bFYtWPHbNg3as3JB4E/A94GHMfYfehbwCtrTdDdd0Tvu4H/AU6rddrZZP26bgbTYZtzPp+nfyjDYDrL+nXddZv/SC7HYDpHPh/+Kx/J5eo2/5mkHmVZPI/WClu9AbEa9oh4YDxnVQf9QxlWL0kRj9mk8nZhzyrKdZ1MBGEn4vXrusct84JkjFze6WiNl02nMH4iMLJ5ZzCdA3cWJhMcGM5y3PI2+ocyLGlvYdmCZDhf99F/xoXO9JXKbiSXwx3y7qSzebK5PM8+asGE055zctdoGrWmVykP5baBC3tWMZjOsrA1Rt7DpsRcPmzWqLY/lNt/IM9xy9pZu7x9tKYrk8vREg+qrtPiebXEjJGSPoDV/l+X1iw4hzvXd6bi49K9sGcVuXy4DvL5MDjI5Z0Le+p3Mc6RKs1jQUsw/sIBB1ri1ddVIxVvn8cta+PgcHZ0f1m7vJ2RaP1X2ucmOgbX+zxQLc2pnhPWr+serdHK5T3chzwMQCtNW26/PqqjBQj3wXw0HwfOO2XFhNtttf2x1mPHbNg3auo0b2Y7gSvc/dNmFgMyhB3ff2FmZwPfcPdFNcynHQjc/UD0+RZgg7vfXGma2dppHnSVYj3pKkVdpairFEMz/Uos0FWKxXSV4vy7SrFSp/laA64h4JXu/v0yAde5wLXuvrCG+RxHWKsF4S0pvuzu/1RtmtkccImIiMj8UingqvU+XFuAC4Dvl/ntXOAXtczE3R8FnlNjmiIiIiJzQq0B1z8C3zCzFPB1wqb355rZ7wFvAV7VoPyJiIiIzHo1dZp3928BbwReBtxE2Nfzs8BFwB+5+3cblUERERGR2a7mR/u4+7XAtWZ2PLAM2Ac84M2+Vb2IiIjILDPpZym6+4PAgw3Ii4iIiMicVFPAZWaXV/k5DwwA97j77XXJlYiIiMgcUmsN118BrUB79P0gsCD6PBjNJ2lmvwTOdffJPShNREREZA6r9U7z5wE7gT8AUu7eAaSA10fDXwacASwHPtqAfIqIiIjMWrXWcP0ncKW7f70wwN3ThJ3oFwL/4e7PN7N/JLyFhIiIiIhEaq3hOhV4ssJvO4ETo8/3AxPecV5ERERkPqk14HoQuNTMWooHmlkS+BvggWjQUYD6b4mIiIgUqbVJ8VLgBmC7md0C7CHsr/Vywo7050XjPQ/4Zr0zKSIiIjKb1RRwufttZraWsDarB3g+YRPjNcC/ufuOaLzLGpRPERERkVlrMnea3wG8q4F5EREREZmTau3DJSIiIiJTVHMNl5n9AfBnwPGEN0Edw91X1DFfIiIiInNGTTVcZvZGYCPwMHA0cD3wnWj6AcL7dImIiIhIGbU2Kb4L+ADw1uj7J9z9YuBYYC9wqAF5ExEREZkTag241gJ3unsOyAEdAO5+APgQ8LbGZE9ERERk9qs14OoHktHnPg7fWR7AgKX1zJSIiIjIXFJrp/lewsf7fJew/9blZpYFRoDLgbsakz0RERGR2a/WgOufgWOiz5dHnz8BxICfA5fUP2siIiIic0Otd5r/KfDT6PN+4ILoOYpJdx9oXPZEREREZr8J+3CZWauZpc3s1cXD3T2tYEtERERkYhMGXO4+DOwGso3PjoiIiMjcU2sfrk8Df21m33X3zJEmamYxwo74fe7+iiOdX73dt7Ofm7fsom//EKsWpTjn5C6+3ruNa3v7GM7kIe8kWwIwI5UIaEsE7DmYYSiTHzOfGOE9NAASARy7vI1YEMMwnre6kzetO4YTV3Zy385+/v37D/Hzx58mk3O6F6c4bnmKnz/eT/9Qhs5UgvXrunnLmWvHzP+GzX188Ib76OtPA9ASM047djEvWbuMW+/fy66BYbLZHANDWQ6N5LDAWJiMcfTiNo5a1MpJKzs5MJzm5l/tHpPOGcevGJOf5e0Jjups5alDmdG8v+hZS7nz4af4vyf2MzySY//gMP3pPA6k4gGvP+1oLn/lKaP53LhpG7sGhunqaGX9um7OP3UVG759L9f29nEwnSMAEjFIxGOMZHKMREVpwOK2BMvaEzy+7xAjuXDY8o4WTj16ESet7OSck7s4cWUnABu+fS/X3LmN4jVhgE+wztvicGgKfymOWdJK3IxtTw+Td3B3YgE4RjZ/ONUA6F6SZCRv7No/PLpdFGuJGW/6rdVc/spTuG9nP5ddt5lf7Rwgm4d4AC3B5PJoBgmD1kRAOueAsXpxijOOX8qWHQfZNTDMgmScY5akaEsmRrf1QlkW3LC5j49+70H69g+Rzo4tyfaWgGOWLmBJKs6TA8PsGRwhm8sTYGTzeTAbt18ALE7F+Y1VHbQm4gwMZ+lMxcesy0u/0suN9+4mm3cC4Fld7SxKtfDE/iFwOHpJ2+h2BPDp2x/iX777IIWkAoOuhXF2D2RHyzow6GxNcMzSFLk8PLJ7oKbyNGBFRwuXv+Kk0fSKjxEPPTnAr3YMkJ1oI5uEmEFXZyu/eUwnOwcyY/add137yzH5bovDy3+ja1x5nXL04tF1CnDzll389JG9PLF/iJFMjpZEjO4lbbzw2KUV1/snbnuU7U8fIhELOG3NYp6zunP02LIgGWdxKs72/UMcTOcYTo8wnAN3I2bO4gUttCbidHW00haH3m39HEyX2/LDdRM3wIy8h9vshT2ruPyVp/Dp2x9i46Zto8eo3zymk96t+9k5EB73Ah9bG5AIYGEyxr6h3Jj5L1+QYGVniu37hxkaydHeEqOrs5Un9x/iqUNZHDCHVNGxPWbOngNZ8hSORXE621pIJeI8b3Un3UtTXH/Pk2PK6PxTV/Kde3by88ef5uBwhpGs44THoNZ4wNquBfz2s7vGlXnxNtUSMwxI57ziOiy3H1Q61pau19JxAN77zc3sHz68r6YSAQuTAU8fypHNO/HAWLOsjfNOeQY/fnAXdz8xcQNXMma0JgL6h8uvd4BEzFi+MMlTA8Okxx8qRoXbB8SCgNWLU/zNy9eWXbZyy/H2lz1r3PlzOpj7xEcJM/sX4I2E28ytwC7GnsPc3d9dc6Jm7wB6gI6JAq6enh7v7e2tddZH7L6d/Vx9x2N0phIsbI1zYDjL7ffv4vGnh4gHRi7nowfwloDRwKBWy9oTPGNRioPpHMcua+f3n/8MvnDnVu5/8gCt8QAzePpghizQ1hLQ2RpnKOOM5HJcevbhjeaGzX38/XWbGSiTgURgHL0kRXoky46BEWBs0NESM57R2UoqYTy4+xBtLTEWJGMMZZyhTIbl7S3sH87RGg/I5vMMDIVRzurFrXSmWnjq4AhDmSxtLQlSCeOxPYdGD3hBlI4ZXPSibl5wzBKuvOkB2pNxFiZjHEjnGExneeayFD96eB/uMMkiHLUoFePM41cQBAGXnHEsX+/dxufv3DbFuR2ZWoK6Wufzqud0sfmJAR7bN1SHOYbCgNZI5xwDVnYmWZRKsH3/MPm885K1S+nqbKN/KMMlZxw7eiK4YXMf7/ufLRxI58jkyy9hIoBsPjypJWOHg8JaymT5ghaWLkji7jz7qIUEQcDTB4e47aF9RMfX0e0jHsCCZILAnM62FrI557JzT2D704f455sePOIymsiCZIwPveYUjlu+YPQY8YutT7F5x4GGprt8YQtrlrRxIJ3j/icrp1VaXsevaONFz1rB408NEpiRzeX4v2395N0ZyeVJxsMGjhccs5jF7clx633Dt3/NUCZPayIgm3MODmfAjFWLU6TiAVufOsRQJk9bS0A6ky8bcK5YkGA4k2Og2pm0RNwgCIxc3nnu0R3ct+sgLbEYqYSx/1CGoSihwKDCJllVKg55N7JFx/JS1Y7tLTFYs7SdpwdH2D+coSUeo70lRjbnDI1kMTMADGcwMz6DBrzgmE5WL1kwWubF553hTJafP/Y0DrzwuMUk4/Fx6zAIbNx+AJQ91l527gljArLScXb3D7F/KDvhcbiwPycDqgZGjRYPi5fOthY2XHDSmGV7x1d/WTZvMYO/O+f4pgVdZna3u/eUDq/1PlyvAdKEt4F4CfBa4HUlr1ozcjRwPvDZWqdpppu37KIzlaAzlSAwozOVYHv0byIZD8bsoJMNtgD2HcqQaomzsDXO3oNpNm7axhP7hmiNx0gmYrTEY6MbfibnBEFAezJGSyzGxk2Hg4mNm7ZxKJsfPcgGFr4DZPLOSDbP3oOZ0WEejVOY71AmzyN7hohFAwvpQEBf/8hofnJ5CIJw3k8NhnnP5sPpM7kchzL5sW3NBvHAwOHa3vCfVHsyHpZnENCZStCejHPHQ/tG0zYmpzB+/1COJwfSdKYS3LxlF9f29k1yTvWTiE12Kcpz4MZ7d7N9/xABY9frkcgDLfFg9KA5MJylfzhLKhGQTMS4p29gdLu/ecuu0ek2btrGUDZPPFZ53pmwIgsDhrOM2eaqMeDpQyN0phK0JmKj6/KOKNhKxMKZFuaXzUN7MkYiFmMkm6c9GWfjpm1j9otGOpjOsXHTtjHHiPuePNjQNA3Yfygzuu9UU1peD+8+RGcqwb7BEfYeTPPQnsFwGzBG972WeIwHdx8su96z+bB2IBELSLWEx6VsdGzpH86G69wgnS0fbAHsHcxwcJJn52x0rI0Fxt1PDNASi9GejBEEAbmiWcWDqe0Z6RzEY0ZQNH3pibDasX0kB4MjOQ5lcmF+3EfLqFCj6ziZMtFcYf+7/8mxZV68TT269xALWsNzxKN7yq/DSvtBuWNt6XmjdJyhbL6mYMssPB5NZ7AF4bEsEQ8YHMmMW7ZKecs5TTtOVFPrVYrH1jHNfwP+DlhYaQQzu4ToVhPd3d11THpiffuHWNk59tncWa/PSQ8O/yNLxgMGhjPsH8ownM3Rljh8Riscu/JFf99SCaN/6HBr7q6B4XH/7sygUGEZHgTL/7tyIJvPk3GnLVbS9BXGSqMBRC76yR0y0dEu504u72TzTs7HbuHujB69hjN5dg0Ms2JBy5hxFibDg3cioOxBqRYB4Y43MJxhYWucvv1DYXPvHJDNhxtczKZe+zeRTC5POpunJWbEAhhMh2FzoSwLCttZeHKrHEIVajYnW+FQOIEW9oeFrXHyHP4XW04sMNLZPM9Ixtg1MDxmv2iUwva2a2B4zDGi3D5WTzGD3BSqcYprukayYQAwmM7Rlgg4lAn382zeWRg3BtPZsuvdOByYQbhvu4fHFgiPZYFBvsJGGjC1GqiCRBAGN6nE4TxM8XAxhnuZ2tdJVlGns3miYhi3fjxKo1xeC8fodDY/psyLt6mDw1kWJMPzwcBwuG2XrkMYvx8AZY+1hd+AssfjWteRe1jLfCTrtB7co2WPzi8FxZ/LacZxYiK11nDVhZm9Atjt7ndXG8/dr3b3HnfvWb58eZNyF1q1KMWB4bEdO6od/CercPxKZ/Mk4zG6OlppjcfI5A5vxYXkiv+BDWV8zD/cro5WSv/gFR/7k/GAuI3PuEfzjwcBCTMy+bH/FKNz/Wh+ChU3ZpCIRTu6GbHAiAc22iwxmveiA1drIqCro5UDJf02DkR9tjL5qQeyhT4VHa0JDgxnWbUoRWuiqZtzw8QDIz7F5pJaJWJBWGObd9JZpz0Z/vcqlGVBYTvLTxBYFNbHZNdntEmRzuZH12XA4UC/nFzeScYDDqRzdHW0TljzUw+FmKKro3XMMaLcPlZPOR8b9NSq6H8PLfGAZDysJcrkPAzkoyC6sO7LrXdnbDBRqNFKxsNtp9CkF1TY7fKEx7up7pWF/09DRc1yVSpaa2Z2+Dg4apL7WjIeUDj0la6fQm1QubwWdqNkPBhT5sXb1ILWOOlsfnSfgPHrEMbvB5WOtV0dhysQyo1T6+ZlVn2/bBaL/oTEYjZu2appxnFiIjXvC2Z2qpl9zcweiW4T8fxo+D+Z2bk1zuZ04FVmthX4KvDbZvbfk851A51zchf9Qxn6hzLk3ekfynD0ohREVefFO1HLFI4kS9oSDI1kOTCcZdmCJOvXdbN6SYrhbI50JsdINje6UhIxI5/PM5jOMZLLjXZuBFi/rpu2eDBaq5D3w8eMRGC0xAOWLUiMDjMOn8ATsbBD6DOXp0YPqIV0IM+qzpbR/MSC8B+sA0vbw7zHA4uaGmK0JYKx1aR+uIbmwp5VrF/XzWA6G5ZnPk//UIbBdJYz1i4ZTXuy+3Bh/M5UjKM6kvQPZTjn5C4u7FlVdbpGytTpSGTAeaes4OhFKfKMXa9HIqDwL7kQqMaj/oF50pkcz1nVMbrdFzroQridpeIB2SpVC4kgqv0AWuOM2eaqcWBxWwv9QxmGM7nRdXnG2iU4UZkWLX88gMF0jkwuR0s8YDCdZf267jH7RSMtSMZYv657zDHixKMWNDRNBxa1JUb3nWpKy+tZK8I+eUvaW1i2IMna5e3hNuCHA6mRbI7jVywou97jAVHXgTxDI+FxKR4dWzpb46M1XuGfu/J5WtaeYEFycgfKeHSszeWdF6zuYCSXYzCdI5/PjwbowJia+clIxiCb8zEtCKWVdNWO7S0xaG+J0ZaIhfkxGy0j3EklAgwjUSbiKux/zz5qbJkXb1PHLWvj4HB4jjhuefl1WGk/KHesLT1vlI6TigcTBgKFWru8h324plMAZLJ52lsS45atUt5iRtOOE9XU2mn+XMJH+vwE+AHwfqDH3X9hZpcDv+Xu500qYbOzgHfOtE7zoKsUdZVi7XSVoq5S1FWKukpRVymWN1+vUqzUab7WgOuXwM/d/c/MLE7Yeb4QcL0K+JS7P2OSGTqLGRpwiYiIiEzFkV6l+Gzga9Hn0ghtAFgy2Qy5+20z8R5cIiIiIvVWa8C1Gziuwm+/AUz/9ZYiIiIiM1StAddXgQ1m9uKiYW5mxwPvBr5U95yJiIiIzBG1PtrnfcBJwO3Ak9GwbwFHAd8DPlj/rImIiIjMDbXe+DQNvMLMzgbOBpYB+4Bb3f2WBuZPREREZNartYYLAHe/lfBZiiIiIiJSo5r6cJnZVjP7kJk9r9EZEhEREZlrau00fx3wB0CvmT1oZhvM7OQG5ktERERkzqgp4HL3d7r7GuDFwI3AxcA9ZrbFzN5nZs27hauIiIjILDOppyK5+yZ3fzuwGjgLuA34K+C+emdMREREZK6Y6mMo24Fu4BigE0jXLUciIiIic0zNAZeZpczsQjP7BuGd5z9H+Jifi4GuqhOLiIiIzGM13RbCzL4GnA8kgR8AbwX+x933Ny5rIiIiInNDrffh6gLeCVzn7nsbmB8RERGROafWO82f1eB8iIiIiMxZNd9p3swWAW8hvDXEEsJH+/wIuFpNiyIiIiKV1Xqn+WcCW4ANhFcoboveNwCbo99FREREpIxaa7g+BjwNvNDd+woDzWwVcBPwr8AF9c+eiIiIyOxX620hzgIuLw62AKLv/wC8tM75EhEREZkzag24HIhVmYfXJzsiIiIic0+tAdcPgQ+Y2THFA6PvG4Bb650xERERkbmi1j5cbye84elDZvYLYBewAngB8ATwjobkTkRERGQOqKmGy923As8G/hr4FZAAfg28DTgx+l1EREREyqj5PlzuPgJ8KnqJiIiISI1qvQ/X2WZ2UYXfLjIzXaUoIiIiUkGtNVz/BPxPhd+WEd6Bft1EMzGzVuAOwodgxwmfzfj+GvNQd5d+pZcb791NNu/EA+O8U1bwO7+xkg/ecB99/elx4wcG+Tpcj2nAwtYYb33pM3nLmWvZ8O17+fyd2yqO3xIz3vRbq7n8ladww+Y+Lv3yL8keeTbKWrMkyf5DGfYP5yuOs6Al4OwTl3P9PbuO+PLUAKic0uTFgFwd5yfNdWJXGzf9zUt5zcd/xN1PDEx3dsa44DldbN83NOPy1UxT3V/rvZ/PFEtSMfYNHdkRJ2aw7plLiBvc9tC+KU3/ilO7uOoNPdywuY+Nm7axa2CYro5W1q/r5vxTVwGw4dv3cm1vHwfTYX7NoL0lxoU94e/X3LltVq2jmEFXZ7iMbzlzLcCE51KAZMx4x++sHZ2mmcx94lOmmR0AXu3u465GNLOzgW+6e2cN8zGg3d0PmlkC+DFwqbv/tNI0PT093tvbO2EeJ+vSr/TyrXt2YYQrLufhvS2aeWBIBMapqxbWdAA34My1S6a0Q4rMJnFo2B8KkbnqBas72H0wQ3syzsJkjAPpHIPpLJedewJ3P76PjT/ZRq7kdG/M7ns6xQySiYBLz34WuwaGJwy2CgKDd59zfMOCLjO72917xqVb4/RZwucnlrO01kx46GD0NRG9pmV933jvbgxIxIwgMBIxA5oXbBmQyXvN/5YduEPBlswDCrZEJu/uJwZoT8bpTCUIgoDOVIL2ZJyNm7ZxbW8fscDGTTObgy0IK0paYrHRZaxV3mHjptqCs3qqNeD6MfAuM2spHhh9/1vCh1jXxMxiZvZLYDdwi7vfVWacS8ys18x69+zZU+usJyWbd2Ljt7+mmUrSs6m6V0REmmthMjbu+66BYYYzeRK1nu1nmVTC6B/KMJyZ3BmyfyjToBxVVusqeC/hbSEeNrOPmNk7zOwjwEPACcBltSbo7jl3fy5wNHCamZ1cZpyr3b3H3XuWL19e66wnJR7YuOrVZppK0nN0fxERkTo4kM6N+97V0UprImCS8cisMZRxOlMJWicZUXamEg3KUWW13odrM/CbwJ3AHwEfit5/DJzm7lsmm7C77wduA86Z7LT1cN4pK3Agk3PyeScTRV/NCmqcsA/XC1Z31DS+AWesrdSqKzJ31HyvGhEZ9YLVHQyms/QPZcjn8/QPZRhMZ1m/rpsLe1aRK3PF1zQ28tRFzGAklxtdxloFBuvXdTcwZxXSrXVEd3/A3d/g7ke5eyJ6/0N3f7DWeZjZcjNbFH1OAS8D7p90ruvgqjf0cMFzukZruuKBccFzuviPNz6XVZ3JstOUaQKfEgM6WmO883fX8o23voSLT6++4ltixptP7+aaP1nHx9/43IaekNYsSbKotfpmsaAl4ILndNVlZ613gFvpgZ8yO5zY1cbDV55f8x+RZrrgOV0zMl/NNNX9da7Wzi9JHfkRJ2bw4mct4awp/qGOWbhtfuOtL+Gyc0+gI5Vg98EROlIJLjv3BM4/dRWXv/IU1r+omwVFTY5m0J6McfHp3Vx8evesW0eFqxQvPftZvOXMtVz+ylMmPJdCeJViIzvMV1PTVYp1S8zsVGAj4XkxAK519w3VpmnUVYoiIiIi9VbpKsWm1t5HTZPPa2aaIiIiItNtttUiioiIiMw6CrhEREREGqwuAZeZddVjPiIiIiJz0ZQDLjPrNLOLzewWYHsd8yQiIiIyp0yq03x0K4dXAW8gvH9WAtgCvKf+WRMRERGZGyYMuMwsThhcvYEw2GoDdhIGW2909681NIciIiIis1zFgMvMXkoYZL0GWEz47MONwFeAXwNPEQZeIiIiIlJFtRquWwmfQPMDwkf5/MDd8xD232pC3kRERETmhGoB14+AFwMvBVLACWZ2nbvvakrOREREROaIilcpuvuZwGrg3UAS+A9ge3RV4psJa79EREREZAJVbwvh7jvc/aPu/pvA8cAHgGcA/0r4DObLzey1Zlb+ac8iIiIiUvt9uNz9YXff4O6/Qfg8xI8AzwSuRZ3nRURERCqa0o1P3f0ed3+3ux8LvAT4Un2zJSIiIjJ3TOrGp+W4+53AnXXIi4iIiMicVO0+XJ+fxHzc3f+kDvkRERERmXOq1XCdUsP0rcBvEF6xqIBLREREpIyKAVd0ZWJZZtYO/CXwDmCE8A70IiIiIlLGZB9e3Qn8dfRKAZ8FPuLufQ3Im4iIiMicUFPAZWbLCGuz/pLw/lufBD7q7nsamDcRERGROaFqwGVmK4F3AW8BhoGPAVe5+/7GZ01ERERkbqh2leIngYuA/cAVwCfcfbApuRIRERGZQ6rVcL0len8KeB3wOjOrOLK7n1bHfImIiIjMGdUCri+iB1SLiIiIHLFqt4W4qIn5EBEREZmzjvjRPgBm9kJ3v6uG8VYT1pwdBeSBq939qnrkYSpu2NzHxk3buH/HfgbS+enKRk1WdsTZ9J7f5aLPbeK2h/ZNd3ZEGiJGeGBQ1bqINMpZa5dwzZ+sa3q65j71Q5uZvYLwKsYXu3ushvFXAivd/RdmthC4G3i1u/+60jQ9PT3e29s75TxWcsPmPq686QEOpTM8dShb9/k3QkB4MhIREZGpa2TQZWZ3u3tP6fCgygQxM3uPmd1vZoNmtsXMXhf9do6Z3Qt8C1hOeDXjhNx9p7v/Ivp8ALgPWDX5xTlyGzdtoz0Z58BwbjqSnxIFWyIiIkfujmloKarWpPi3wD8C3wX+FzgG+KKZvZDwJqgPAH8AfMOnUE1mZmuA5wHjmiLN7BLgEoDu7u7JzromuwaGWbGghewR1PCJiIjI7DMdFRjVAq6LCO8m/67CADN7A/Al4NvAa9x9Sm1xZrYA+AbwdncfKP3d3a8GroawSXEqaUykq6OVgaEMcTNGFHSJiIjMGxWb96YpzTXAd0qGfTt6/7cjCLYShMHWl9z9m1OZRz2sX9fNYDrLwtYJu57NGNOxgYiIiMw1Z6xd0vQ0q53DW4GhkmGF7/1TSczCO6d+DrjP3f91KvOol/NPXcVl557AM7s66EjO/FBmZUecR688n7OmYSMRaZYY4cNaRUQaZcZdpWhmeeAjwONFgwPgKuDDwBNFw93dPzlhYmYvBn4E3MvhJtT3uPuNlaZp1FWKIiIiIvVW6SrFie7D9a4Kw99d8t2BCQMud/8x+gMrIiIi80y1O83P/HY2ERERkVlAQZWIiIhIg9X0aB8zq3YzrDwwUO72DiIiIiJS+7MUtzLB483MbBvw7+7+sSPNlIiIiMhcUmvA9UbgQ8AW4HpgD+EjfS4ATgY+CPQAHzYzFHSJiIiIHFZrwPUy4Hp3/6uS4Z82s/8AXuTuf2xmB4E/BxRwiYiIiERq7TT/OsIHVZdzPWFNF8BNhM9cFBEREZFIrQHXMHB6hd9Oj36H8B5bg0eaKREREZG5pNYmxauB95nZUsLnKRb34fpz4J+i8V4E3FPvTIqIiIjMZjUFXO7+PjPbR3jn+bcRXrFowJPAu4o6yX8N+HwjMioiIiIyW9Vaw4W7f8zMrgK6gS7CYOsJd88XjfOr+mdRREREZHarOeACcPe8mT0OjAC7i4MtERERESmv5kf7mNl5ZnYXYQf5bcCp0fCrzexNDcqfiIiIyKxXU8BlZn9MePuH+4FLSqZ7CPiT+mdNREREZG6otYbrvcBH3H098N8lv/0KOKmuuRIRERGZQ2oNuI4Bbqnw2zDQUZ/siIiIiMw9tQZcTwDPq/BbD/BwfbIjIiIiMvfUGnB9Dnh/1Dk+FQ0zMzsb+DvgM43InIiIiMhcUOttIT4ErAY2Arlo2E+AGPBpd//3BuRNREREZE6o9U7zDrzVzD4GnA0sBfYBP3D3BxuYPxEREZFZb7I3Pn0Y9dcSERERmZSaAy4zawEuAk4DVgI7gbuAje4+0pDciYiIiMwBtd749ETCG5x+HDiZsB/XydH3h81M9+ESERERqaDWGq6rgX7gJe6+rTDQzLqBG4BPAWfUP3siIiIis1+tAVcP8IbiYAvA3beZ2eXAl2uZiZl9HngF4YOvT55UThvghs19bNy0jZ89tm+6syIiIiJNsvXK85ueZq334doKtFb4rZXwYda1uAY4p8ZxG+qGzX1cedMDCrZERETmmTWX3dD0NGsNuC4D/tHMXlg80Mx+C9gAvLuWmbj7HYS3k5h2Gzdtoz05qYs0RURERKak1ojj/xE+L/EnZrYb2A2siF5PAe8xs/cURnb3044kU2Z2CXAJQHd395HMqqJdA8OsWNDSkHmLiIiIFKs14NoSvZrC3a8m7KhPT0+PNyKNro5WBoYyjZi1iIiIyBi13mn+zY3OSLOtX9fNlTc9MN3ZEBERkXlg3nZiOv/UVQC6SlFERGSemY6rFJsacJnZV4CzgGVmth14v7t/rpl5KHb+qatGAy8RERGRRmlqwOXub2hmeiIiIiIzQa23hRARERGRKVLAJSIiItJgCrhEREREGkwBl4iIiEiDKeASERERaTAFXCIiIiINpoBLREREpMEUcImIiIg0mAIuERERkQZTwCUiIiLSYAq4RERERBpMAZeIiIhIgyngEhEREWkwBVwiIiIiDaaAS0RERKTBFHCJiIiINJgCLhEREZEGU8AlIiIi0mAKuEREREQaTAGXiIiISIMp4BIRERFpMAVcIiIiIg2mgEtERESkwRRwiYiIiDRYvNkJmtk5wFVADPisu1/Z7DwUrLnshulKWkRERKbB0raAuy8/t+npNrWGy8xiwMeBc4GTgDeY2UnNzEOBgi0REZH556lDeV6w4aamp9vsJsXTgIfd/VF3HwG+ClzQ5DyIiIjIPPbUoXzT02x2wLUKeKLo+/Zo2BhmdomZ9ZpZ7549e5qWOREREZFGaHbAZWWG+bgB7le7e4+79yxfvrwJ2RIRERFpnGYHXNuB1UXfjwZ2NDkPIiIiMo8tbWv+TRqaneLPgbVmdqyZtQCvB65vch4A2Hrl+dORrIiIiEyj6bpKsam3hXD3rJm9Dfgu4W0hPu/uv2pmHoop6BIREZFmaPp9uNz9RuDGZqcrIiIiMl10p3kRERGRBlPAJSIiItJgCrhEREREGkwBl4iIiEiDmfu4+47OKGa2B3i8wcksA/Y2OA0JqaybS+XdPCrr5lFZN5fKe3KOcfdxd22f8QFXM5hZr7v3THc+5gOVdXOpvJtHZd08KuvmUnnXh5oURURERBpMAZeIiIhIgyngCl093RmYR1TWzaXybh6VdfOorJtL5V0H6sMlIiIi0mCq4RIRERFpMAVcIiIiIg02rwMuMzvHzB4ws4fN7LLpzs9sZWZbzexeM/ulmfVGw5aY2S1m9lD0vrho/L+PyvwBM/vdouEviObzsJn9u5nZdCzPTGNmnzez3Wa2pWhY3crXzJJm9rVo+F1mtqapCziDVCjrK8ysL9q+f2lm5xX9prKeIjNbbWY/NLP7zOxXZnZpNFzbdgNUKW9t383i7vPyBcSAR4DjgBbgHuCk6c7XbHwBW4FlJcM+DFwWfb4M+FD0+aSorJPAsdE6iEW//QxYBxhwE3DudC/bTHgBZwDPB7Y0onyBvwQ+FX1+PfC16V7mGVbWVwDvLDOuyvrIynol8Pzo80LgwahMtW03t7y1fTfpNZ9ruE4DHnb3R919BPgqcME052kuuQDYGH3eCLy6aPhX3T3t7o8BDwOnmdlKoMPdN3m4t36xaJp5zd3vAPaVDK5n+RbP6zrg7Plau1ihrCtRWR8Bd9/p7r+IPh8A7gNWoW27IaqUdyUq7zqbzwHXKuCJou/bqb7xSWUOfM/M7jazS6JhXe6+E8IdHVgRDa9U7quiz6XDpbx6lu/oNO6eBfqBpQ3L+ez0NjPbHDU5Fpq4VNZ1EjU9PQ+4C23bDVdS3qDtuynmc8BVLurWPTKm5nR3fz5wLvBWMzujyriVyl3roz6mUr4q++o+CTwTeC6wE/hoNFxlXQdmtgD4BvB2dx+oNmqZYSrvSSpT3tq+m2Q+B1zbgdVF348GdkxTXmY1d98Rve8G/oewuXZXVPVM9L47Gr1SuW+PPpcOl/LqWb6j05hZHOik9ma1Oc/dd7l7zt3zwGcIt29QWR8xM0sQnvy/5O7fjAZr226QcuWt7bt55nPA9XNgrZkda2YthB38rp/mPM06ZtZuZgsLn4HfAbYQluX6aLT1wLeiz9cDr4+uZjkWWAv8LGo6OGBmvxW1+f9x0TQyXj3Lt3herwV+EPXNEEZP+gW/R7h9g8r6iERl8zngPnf/16KftG03QKXy1vbdRNPda386X8B5hFdqPAK8d7rzMxtfhFd53hO9flUoR8J2+1uBh6L3JUXTvDcq8wcouhIR6CHc2R8B/pPoSQjz/QV8hbCqP0P4D/JP6lm+QCvwdcJOsT8DjpvuZZ5hZf1fwL3AZsITykqVdV3K+sWEzU2bgV9Gr/O0bTe9vLV9N+mlR/uIiIiINNh8blIUERERaQoFXCIiIiINpoBLREREpMEUcImIiIg0mAIuERERkQZTwCUyD5nZOjP7qpltN7MRMxsws5+b2QdK7ssz40T3fvt7M/uFmR0ws2Eze8DM/tPMnjXd+YPw0Slm5mZ2UdGwa8xsawPTXGRmV5jZ8xuVhohMXXy6MyAizWVmfwt8BPgh8P+AR4EFwIuASwjvsXPutGWwiigY/D7wDML7//wYGAFOAi4GTid8RtxM9AHgqgbOfxHwfsL7h/2igemIyBQo4BKZR8zspYTB1lXu/jclP99oZv8MvK75OavZfwErgdPc/aGi4T80s08AFzQrI2aWdPd0reO7+yONzI+IzGxqUhSZX94N7I3ex3H3QXe/pniYmbWZ2YfM7LGo+fExM3uvmQVF45wVNaG9Kmra22tme8zsv81sUcn84lGT4P1mljazHWb2UTNrrZZxMzsNOBv4YEmwVci7u/v/Fo2fMLN/NLOtUb63Rt8TJfNdaWZfjPKcNrPNZvamknEuipbvDDP7upntB+4qKp9PmNlTZnbQzK5n7LPmCvMY06RY1Oz4FjPbYGY7zWy/mX3bzI4umfb1ZvaDqEwPmtn/mdn64nkBj0VfPxPNt7RJ8/fN7KdmdihK5+tm1l2tzEWkflTDJTJPWPgw2TOBb7r7yCSm+S5hk90HCB8B8lvA+4AlwN+WTHIV8B3gjcAJwIeBHIefrwbw38ArgQ8BPwFOjOa9BnhNley8LHqv9ZmnG4ELgQ8SNj2uI2xCPS7KX+H5n7cDi4H3AE8AbwL+y8za3P3qknl+ifDxP6/l8PHz08AfAP9A+IzWlwNfrjGPAH9PWA4XAyuAj0bpnFk0znHAdcCVQB44A/ismaXc/VOEjyP6feCbwD9zuIweiZbzz4FPAl8ANgALgSuA283sVHc/MIn8ishUTPezhfTSS6/mvIAuwmep/XOZ3+LFr6LhfxRNc0bJ+O8l7Du1Ivp+VjTexpLx/hMY5vCz1l4SjffHJeP9YTT8uVXy/8lonGQNy3pyNO4VJcP/XzT81Oj726LvZ5WM931gNxCLvl8UjfexkvFOIAwoL6uQ14uKhl0DbC36viYa5/aSad8ZDX9GhWULovX0GeCeMvP705LxFwD9wOdLhq+J1uHbp3vb1Euv+fBSk6LI/GFlB5odRfiw5tFXVLMFcA7wOPCTqCkwHv32PSBBWNtV7IaS7/cCScJgrzC/EeAbZeYHYc1NPRTm898lwwvfzywar8/dbysz3nLCmr1i/1Py/YWEAdC1JcO/Oom8liszgNHmPjNba2ZfMbM+Dq+nPyUM+CayDugAvlRS5tuB+6lfmYtIFWpSFJk/9hLWNpX229kL/Gb0+RLgz4p+WwEcQ3iCL2dpyfd9Jd8LncoL/bNWAC3AwRrnV+yJ6P0Y4MEq40HY3AlhU1uxJ0t+X1JmnHLjFZSOW7iFxq6S4aXfq6laZma2ALgFOARcRthMOAL8BWEz5ERWRO/fr/D705PIq4hMkQIukXnC3bNmdgfwcjNr8agfl7tngV4AM3tFyWRPEXbGvrDCbLdOMhtPEQZ9L6nw+44q034f+CfC/l8fnSCdQhBzFFE/pqLvhXwUxitXS1Q6XoGXfC8EYF2Et9eg6Hu9rCMMMl/i7j8uDCyqhZxIYRkuAn5V5nf13xJpAjUpiswvHwaWEXZYr8XNwGrgoLv3lnntnWT6NxPW3HRWmF/FgMvdfwbcCryn0g1OzaxwW4jbo/fXl4zyh9H7HUXjHW1mp5eM90bCPlz3TbA8dxF2Yi8NSEvTPRJt0ftoLaOZLWb8LTAKNWOpkuE/IQyqnlWhzB+oY15FpALVcInMI+5+q5ldBlxpZqcCXySswWoFjicMFAY5XJPzJeDNwK1m9lHgHsImwWcCrwJe7e6HJpH+bWb2FeA6M/tX4GeEAcsa4Dzg3e5erbnwjwhrun5uZv/B4RufPpuweS0BfMvdfxWlc0VUE/QTwpqi9wFfcffN0fyuAS4Fvmlm7yXs1/SHhFcavsXdcxMszwNm9mVgQ3SbjMJViufVWiY1+AkwAHzczN4PtBN2/t8LdBaNt4uwNuv1ZraZcD0+5u5Pmdm7oumXAzcRdqJfRdiX7TZ3n8xVlSIyBQq4ROYZd/+wmd1JGGh8kLBz+DDwAPA14FOFQMPdM2b2u4R9hy4BjiU8kT9C2Nm7pttLlHgT8FeEAdJ7CWtmthLefqJq3yd332lmLwT+mvAGrX9LeBzbSlh7Vnwn9/WEzXwXEwYoOwhr9v6haH6DZnYmYc3flYS3S3gA+CN3L+1wX8lbCPukvZMwGP0BYQ3Zj6tNVCt332Nmv0fYjHpdtBxXEfYve3/ReHkz+1PCdfp9wnJ5M3CNu3/azJ4A3hXlLQH0Edb0/bIe+RSR6gqXaouIiIhIg6gPl4iIiEiDKeASERERaTAFXCIiIiINpoBLREREpMEUcImIiIg0mAIuERERkQZTwCUiIiLSYAq4RERERBrs/wOF3G52VqRA4gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "\n",
    "plt.scatter(range(len(kkk[0:])),kkk[0:], alpha=0.5)\n",
    "plt.xlabel('Gene Coordinate', fontsize=16)\n",
    "plt.ylabel('pegRNA coverage', fontsize=15)\n",
    "plt.title('RT Template Length = ' + str(rt_template_length), fontsize=15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#now performing this for each gene and at rt template lengths from 1 to 50\n",
    "path = '/Volumes/Sam_G_SSD/human genome GrCh37 IMPACT genes/'\n",
    "unique_genes = np.load(path + 'human_impact_genes_NAMES.npy', allow_pickle=True)\n",
    "unique_genes = list(unique_genes)\n",
    "\n",
    "rt_template_length = np.linspace(1,50,50) #iterating through all RT template lengths from 1 to 50\n",
    "\n",
    "for x in rt_template_length:\n",
    "    \n",
    "    rt_template_length = int(x)\n",
    "    rt_len = [] #coverage array for holding information\n",
    "\n",
    "    for gene in unique_genes:\n",
    "\n",
    "        gene_name = gene\n",
    "\n",
    "        kkk = prime_domains(gene_name, rt_template_length, df1)\n",
    "\n",
    "        unique, counts = np.unique(kkk, return_counts=True)\n",
    "\n",
    "        rt_len.append(kkk)\n",
    "        \n",
    "    filepath = '/Volumes/Sam_G_SSD/PE coverage NGG npy arrays/'\n",
    "    np.save(filepath + 'PE_coverage_NGG_rt' + str(rt_template_length) +'.npy', np.asarray(rt_len))\n",
    "    print('complete rt template length = ' + str(rt_template_length))\n",
    "\n",
    "    #files provided in dropbox; no need to run this yourself\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prime editing boundaries\n",
    "Performing similar analysis, but this time getting coordinates of prime editing domains.\n",
    "\n",
    "Splitting it up into two arrays, corresponding to (+) strand and (-) strand. This directionality information is required for further analysis. \n",
    "\n",
    "The only information needed is the starting location of the editing domain (as well as which strand it's occuring on), which will be marked with a 1 in the array of zeros. From there, with knowledge of the RT template size and the other prime editing domain array, you can check whether a target mutation (applies to insertions and ONPs) falls within the boundary of a single prime editing domain, or crosses multiple domains (in this case the edit isn't possible with the given RT template length). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "\n",
    "#changing it so that I look +/- 1 kb upstream and downstream of each gene to avoid issues with TERT mutations\n",
    "\n",
    "def prime_domains_boundaries(gene_name, df1):\n",
    "    #df1 is the dataframe containing gene sequence information\n",
    "\n",
    "    gene_chrom = df1[df1['gene']==gene_name]['chrom'].values[0]\n",
    "    \n",
    "    if gene_chrom != 'chrX':\n",
    "        chrom = int(re.search(r'\\d+', gene_chrom).group())\n",
    "    elif gene_chrom=='chrX':\n",
    "        chrom = 23 #it's number 22, but we subtract 1 below when accessing chromosome file\n",
    "        #there are no genes in this dataset that fall on Y chromosome -- ignoring\n",
    "\n",
    "    seq_start = df1[df1['gene']==gene_name]['gene_start'].values[0]\n",
    "    seq_end = df1[df1['gene']==gene_name]['gene_end'].values[0]\n",
    "\n",
    "    gene_sequence_1000eitherend = records[index_list[int(chrom)-1]].seq[seq_start-1001: seq_end+1000]\n",
    "    #gene sequence with 1000 bp buffer on either end to account for prime domains at beginning of gene\n",
    "\n",
    "    length_seq = len(gene_sequence_1000eitherend)\n",
    "\n",
    "    #two arrays corresponding to plus and minus end\n",
    "    zero_array_plus = np.zeros(length_seq)\n",
    "    zero_array_minus = np.zeros(length_seq)\n",
    "\n",
    "    #iterate through sequence\n",
    "\n",
    "    #iterate +- 1000 bp from start/end of gene\n",
    "    for idx, val in enumerate(gene_sequence_1000eitherend): \n",
    "        \n",
    "        by_twos = gene_sequence_1000eitherend[idx:idx+2]\n",
    "        by_twos_true = by_twos.upper()\n",
    "        \n",
    "        if by_twos_true == 'GG': #find NGGs\n",
    "            zero_array_plus[idx-4] += 1#add array of ones at appropriate index when NGG is encountered\n",
    "        \n",
    "        else:\n",
    "            continue\n",
    "\n",
    "\n",
    "    #repeat on negative strand\n",
    "    comp = gene_sequence_1000eitherend.complement() #taking complement (- strand in same orientation)\n",
    "    for idx, val in enumerate(comp):\n",
    "        \n",
    "        if idx+5 < length_seq: #dealing with boundary issue\n",
    "        \n",
    "            by_twos = comp[idx:idx+2]\n",
    "            by_twos_true = by_twos.upper()\n",
    "\n",
    "            if by_twos_true == 'GG': #find NGGs\n",
    "                zero_array_minus[idx+5] += 1#add array of ones at appropriate index when NGG is encountered\n",
    "                #+5 instead of +4 because we're counting from the back of the NGG due to orientation\n",
    "\n",
    "            else:\n",
    "                continue\n",
    "        \n",
    "        else:\n",
    "            continue\n",
    "    \n",
    "    #might be weird edge effects but I'm not using the very end of either of these arrays regardless...\n",
    "    \n",
    "    return [zero_array_plus,zero_array_minus], gene_sequence_1000eitherend\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/5g/xxjcy2g531n02hnyb6d8qdsr0000gn/T/ipykernel_19707/3088350493.py:15: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  np.save(filepath + 'PE_coverage_boundary_NGG_plusminus_1000bp.npy', np.asarray(g))\n"
     ]
    }
   ],
   "source": [
    "#repeating same process as above\n",
    "filename1 = '/Users/samgould/Desktop/FSR Lab/2022-03-17/gene_info.csv'\n",
    "df1 = pd.read_csv(filename1)\n",
    "\n",
    "g = [] #list for holding numpy arrays for each of the genes\n",
    "for gene in unique_genes:\n",
    "\n",
    "    gene_name = gene\n",
    "\n",
    "    kkk = prime_domains_boundaries(gene_name, df1) #marked editing domains for a particular gene\n",
    "\n",
    "    g.append(kkk) #adding it to master list of all genes (indexed by unique_genes)\n",
    "    \n",
    "filepath = '/Users/samgould/Desktop/FSR Lab/2022-06-21/'\n",
    "np.save(filepath + 'PE_coverage_boundary_NGG_plusminus_1000bp.npy', np.asarray(g))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "594"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filepath = '/Users/samgould/Desktop/FSR Lab/2022-06-21/'\n",
    "\n",
    "k = np.load(filepath + 'PE_coverage_boundary_NGG_plusminus_1000bp.npy', allow_pickle=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Next step = quantifying whether mutations in IMPACT data set fall within editing windows at varying RT template lengths\n",
    "\n",
    "variant_type keys: \n",
    "- SNP: Single nucleotide polymorphism -- a substitution in one nucleotide\n",
    "- DNP: Double nucleotide polymorphism -- a substitution in two consecutive nucleotides\n",
    "- TNP: Triple nucleotide polymorphism -- a substitution in three consecutive nucleotides\n",
    "- ONP: Oligo-nucleotide polymorphism -- a substitution in more than three consecutive nucleotides\n",
    "- INS: Insertion -- the addition of nucleotides\n",
    "- DEL: Deletion -- the removal of nucleotides\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SNP    348145\n",
       "DEL     49838\n",
       "INS     17784\n",
       "DNP      6042\n",
       "ONP      1011\n",
       "UNK         1\n",
       "TNP         1\n",
       "Name: Variant_Type, dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mut_types= impact_data['Variant_Type'].value_counts()\n",
    "mut_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yMrDT4X71dBhcxDaqkcuZ3QNvjm/iUubJtNH3ASDRdHIlsYNLbjzLnemj79HKgeeo9X8b+Da1HfZEX5AKSqnlmLa8LZhZLjGKBLjIeYGautWYoL4as0hpvqMFpY5ZxBZnKlPoR9VwExdPacZ1YbsjWB0cM7g2VHjcwNuB1iIv7lWzrLWpLS0lXEAtcAW1/g9T29HiZDFKqdnAD4D/0lrr9OuVkx4kwEXOCdTUFQLvBN4DXAdkw3TomVBq1DYy246c8SK2AyrSO/h7V3FG7QG3gTsSXF8FFAMn37vac67XpfJSU5Yj3g5spdb/QWo7Nqf4exfE7nEPbiO7B/j3uOuj74H/vdb6fsCnlDoU9/i/a63jvy5rSYCLnBCoqZsB3AK8F9NJy4nTo9LNKYeZnOk9cBsdOaKiQ01c3IUZ1Ub1iWB1sCnB9auBXoDLFrucPrgkFeYDT1Dr/ybwT9R26Im+IBm01uOu6tdaP43ZUjbWtZztKCoBLrJWoKZuNiawb8W8CWfaoqrpdkojFzsasS3X6d8D70N3aBW3hcyXUVvIEi1eKwXOBw6fPduauaDYWpqyqpzlAv4BuJJa/x9R23Hc6YLEqSTARVYJ1NQVY6bGP4SZHpe/44mdugrddfoj8E5ldzOyC1umjMBPAr9NcP1CYqv137Uq6xavTcaNmH7q76e24wWnixEjyZubyHiBmro8zAlMHwKqkOnxyVJKazt+p45tR7Ti9E8jO4HdR2Y2cflpsDrYP9aFDRUeC3gHcNJtoc6d6zovtaWljUXAU9T6P0dtx0+cLkYMkwAXGStQU7cO+AJwO+PcHxMJJe0eeIuKholv4pKXMU1cEh1csgzTSa/plpXulT6PypSfaTp4gR9T6z8b+It02WqW6yTARUYJ1NS5MVPkfwrIMYlTE7sHHj8CD5/RPvBWFe3F7NkFF5byqMKkVTl9Xg1WB99IcP1KYv8ouSqQk9PnY/lzYDW1/g9Q29HpdDG5TgJcZIRATd0s4NPA5zBTemLqlNIjW6lGo2Fb4T2TAB9q4uIt8xapzNi4m2jxmg/TKrdlcYkqDJSqlakrK+29E3iRWv8t1HYkOu1OTDMJcJHWAjV1F2BG2x/AHCQhkueUXui2fWYBfiy+C9vMjGji0gv8IsH1czHvj5H3rvZccqYntGWxNcDL1Ppvo7bjaaeLyVUS4CLtxKbJbwW+CFzucDnZbHAb2dBo2Y6Gz6iRyyFruAubuyQj9oDfH6wOjnmc5oYKj8L0CugEuHBBTuz9PhNlmANRvkBtx/86XUwukgAXaSNQUzcHM03+WbK3/3g6OWURWzQattUZjDabVGSoiYur2JUJI/BEi9cWYBawNV27zLW4NF+VpaimTOQBfkStfzG1Hd90uphcIwEuHBeoqTsX+ArwfmSaPJWUGjmDblahn+YIPIzu7lF6OMAL034P+N5gdTBRm9BLiS3Iu365W0bfk/MNav3zgc9S2xF1uphcIQEuHBOoqTsL+DYmuDNh0VO2GXEPXGtbg+Z0A7wbu5ORTVzSfQQ+7l7mDRUeD3At0DojH+/KMuvs1JWV8T4JzI01femd8NliymRhhki5QE3dwkBN3Q+BNzGL0yS8nRFr5GL+/LX5/WkHeLuyQ8TvAc9P6z3gUeCuBNfXAD6g/9Y1nnM8mXemudNuwdwXl74MKSAjcJEygZq6MqAG+BMg3+FyxCmr0E2AW6e5Bey4ig6QOV3YHg5WB48kuH4d0ANQudAle7/PzBXA09T6b6S2o9npYrKZBLiYdoGauiLgz4CvAun85p5rRuwD19q23WcwK3dKFzZvWo/AE+39LgPWAgcumG/NnltkSb+BM7cOeI5a//XUdjQ6XEvWkgAX0ybWo/xzwF9jWlKK9DLiNDKtte05gxXorcoe2gPuKnLlqfSddm4Gfp/g+sWYw130zSs9MvqeurOAzdT6r6a2Y7/TxWQjCXCRdIGaOhdQDfwtsMThcsT4lIqfQte27T6DAG+Jb+JSltZNXO4OVgcjY12IHVxyPXDC68I6Z46VqweXJNsSzHS6hPg0kAAXSROoqVOYBizfBlY5XI6YmEZrNFoNfuLi9FugHlGR4QAv9WTk9DlQDswEmt6zyr063618KaopF0iITxNZhS6SIraX+0XgPiS8M0jcAFzbtvsMTiI7oCJDW4ZcJWm7hez5YHVwd4LrVxG7j3/FEtn7PQ0GQ3yZ04VkEwlwMSWBmrr8QE3dP2mtXwMqna5HnBY9Yrit9WlPodvoyBEVHWri4i5M2zaqiRavFQOXAC0rZqiSxX61InVl5RQJ8SSTABdnLFBTd63WOgjUKKXkdkwm0rZSKA2DI/DTC/A+dIdWcVvIfGm5hawLuDfB9XWAC4i+Z7Vn3eluoxOnRUI8ieRNV5y2QE3dTK31d5RSH5P3uoymVdwgXGttu0/zLPBOZXczsgtbOo7AfxWsDvaMdSHu4JI2BZw/T/Z+p8BgiF9BbcdBp4vJZDICF6clUFP3Ia31LqXUx5yuRSSBxkXsRrjm9O+Bn8TuI/2buCRavLYEWAx0vuMs97LiPFWampJy3hLgUWr9clDMFMgIXExKoKZuqdb6B0qpG2XUnT1iI3Czkk1r23WaU+jNo5u45KVdE5edwergSwmurwfCANctk9F3iq0Gfk+t/zpqO0ITPlucQkbgIqFATZ0rUFP351rrnUqpG52uRyTbcIBrbduu03xPaFXRXmInd+HCUh5VmPQSpybR4rU84GqgZZZP5a+Yaa1OWVVi0KXA/dT6ZTB5BiTAxbgCNXXrtLbrge8oJftis5LWFvEBfpoj8Na4Ji7eMm+RSq/pmQHg7gTXz8EcXxu+bY17rduShZgOeSdwB7X+dPq7kxHkL6w4RaCmLh/4O631nyllyd+RLKbQCjW4Cv30F7Edi+/CNjPturBtClYHjye4fj1mhTqXLHTJ3m9nfQRoAb7idCGZREbgYoRATd0are1Xgb+QrWE5QMevQrdt12kuYjtkRYbuXbpL0m4PeKLp8znASqBt/SLXvFk+a17qyhLj+HNq/X/pdBGZRAJcDFn6td9/Umv7NaWss52uRaSEVqfcAz+9EXiTigw1cXEVp1UXtoPAHxJcr8T83Pqd5dJ5LY38M7X+DzpdRKaQEZYgUFNXoiMDdym3990gt6Fyy/A9cE5zBB5Gd/coPRzghWm1B/zOYHXQHuvChgqPG3g70Orz4F49y1qb2tLEBH5Crf8tajtedrqQdCcBnuOW/sWDlWj9gHJ7Fzhdi0iuI3d+6aPqn29+CvP/8xDwba31RoC3Wvav7OjtvAS0h5NHXA/88Dr8vrIlt3ijZW/19/P+pkZ6tabIsui2bf59wQL+obmZhW4PUQV9tk112cwwBWYPeM+ung+U3VA2ZmA6QAN3JLhegTmXvu3W1Z61eW6Vn5qyxCTlA7+l1n8xtR2HnS4mnckUeg5b8pVf/zWW9bxyeyS8s4wdGSDc/NbHMQuD1mL+v/5ppdQagFlFM5uL8v37AesdC86594Nv+3O6+zo8D3V1rf+P460s93r58swyemybYsvioc5OZrhcFLtd3O4v5RdLl3J3W1sBMNDf3L/SyreO5s3PK3DuJx7hyWB1MNGpV1cBfQCXLZbFa2lqPvAgtX7Z/ZKABHgOCtTUlSz58/sftTz5/6CU5XK6HpF8PdufAGX1Adu11tuBzUAEeBeA3+fv6B3oWaigt8ib33/56iq0tumJRgu9KCIaXurtxQ24tOa5nh6KLIsrfYX0aZsBWxNB29rWA+GT4UvzF+W/kEZNXBItXvMDFwGtq2dZMxYUq0DKqhKn60LgTtleNj4J8Byz5CsPrLXD/Tstb8ENTtcipk/45GGwrF7MYi6ARszU5MLB59g66lWmCYsGUMqi1OXq/Mrs2RyNhHmxN0Spy4VSijluN7eXzuDdfj/P9/Tw6UOHuGp2ydH+w/3r3H73NuVWYSvv1Daq2tbs/eZemr7bdEqN7S+00/D1Bhq+3sBbf/8WvQfMqaSRzgj7/mEfDX/TQOdrnUPPb/qPJsJt4Yl+9DbgNwmuX4hZ6GG/e5X7/PTati7GcDvwTaeLSFcS4Dlk8Z/+/JPK5X7V8uQtnPjZIqPpSV0ZSq89h7dg21G1Jj//4Hyvl0pfIb9YspR7liyly7ZxAT88cZyr3tpLazjC38ydS5HPfTzaHV3pne19s3d/77sP/s9BT2jvyI6YJ/5wgrwFeWMW4p3tZflfLaf878uZs2EOR+48AkBHfQell5ey/OvLOf6w2cbduaWTgqUFeGZ4JvrJfxasDvaNdSHu4JITbgt17lzXuoleTKSFv6XWf7vTRaQjCfAcEKip8yz+0i/ucfn8/6ssl9fpesT085QtBNsuwBzUARDA3Pc9MvgcS7n6Nbhb+jpLf/7Md0ApXZ6XdxJgR38fXzlymKr9+4gAB8JhtIbPzpzJwUiY/9faysPN7fM9czzPDDQPnOOZ7Wlf+ImFNN/fPFRD+GSYrm1dzHjbjDFr9JX7cBWaOzi+FT7CJ2OjaxfosEZHNFigo5oTfzjBrHfOmsyPnujgkmXAXKC7qtx9VqFXpcuUv0hMYabSZbfAKBLgWW7Jn9031+7v2eYqKPmI07WI1Ck85zrQdj5wjlLqbMzCLTewafA5vrziAxryX2ree826ZVfhUi5dkZffBfDY8hV8a958Sl0uFrjdrM7Lo9hlYaGIak2nHaUtHHV5y7xN2HhcPlcegB0eXoh+9OdHmff+eZPamdj2TBvF55o8Lb20lK5gF43faWTOu+dw8smTlF5eipU34dvV68Hq4NYE168gdnDJ1QG3HFySWXzAfdT6i5wuJJ1IgGexhZ/6wbkotd3KK5RDGnKM5fbimbP8TmAOsB0zdf5j4K+3Ht69bOfhN8/v6m1bAaiwtosf3/ZLLGXpv20+dtPga3z92FGWer2szsvjnSUl7BsY4LsnjuNVijylKJ6ftwXAM8ezve9Q38p9397HrBvNKLlzayfuEjcFgYkXpne/2U3bM23Mfd9cAFw+F4E/D3BW7VkULC2gc2snJReVcPgnhznwXwcYPU0fJ9HitQJMgLcsKlGFgVJVMWFhIt1UAD90uoh0orQe/2aZyFzzP/qdmz2zl/3K8uTJNozcdWXjxqrnRj942bJ1O/u1ah2gYG9p+56WuTOXz7nmho0fbz667ZVZr37nyPXFxe+a6IVtdOSy/CP3aMUBgFk3zrqycFXhtYPXj913jPYX2lGWQoc10b4oJReWsPgzi0e8Tt/BPpr+s4nAVwLkzTv1XvnRnx+l5IIS+o/1gw3+9X4O/McBltUsG/3UPmB+sDrYPla9Gyo8lcBngaY/rfSuf/tytyzizFyfobbjR04XkQ6kkUuW8ZVXqpKL3/3neQvXbFQut/z3FQmpU04jm9ysXB+6QyvTxAXA8o1cgT7v9nnMu920F+9+s5sTj5w4JbwHTgxw4HsHWPzpxWOGd/+xfsLtYQpXFdJ7oBfLa0qLn6aP8+sE4a0wB5d0Aly0QPZ+Z7j/oNb/MrUdW50uxGkyhZ5FfOWVrpJL3vv9vEXn/KuEt5gcrWDwNLKobU2yF3qnsrthOMBdBZNro3ryyZOcfPIkAK0PthLpjnDk7iPs/cZe9tbuHfHc5l83M/e9Zlq99NJS2p5rI36afpREi9fmAyuAtqsDrkWl+WpSq+FE2soH7qXWn/OLEOVNPkv4yiu9/ss++Ou8+eU3O12LyCjW4Bqz0xmBn8TuIy7ArfxT94APKlpdRNFqs/Zo5rUzhx5f+PGFLPz4+Dsal3xhydDv3SVuVnx9xXhP3Qc8naDcSwEb4IYVcnBJligH/g94v9OFOElG4Fmg5OJ3l5Re9bFnJbzF6VJ65Glkkx2BN6toGBgY/NzhLmw/CVYHx1zMs6HC4wGuBVr8eXgryuSkvSzyPmr9n3O6CCdJgGe4Gdd+YlHJhRte9c5aconTtYhMNOIeuLYm+Z7QqqK9mC5u4MJSHlU4bSUmFgXuTHB9NVAI9N+6xnO2x6WkD0J2+Tdq/eVOF+EUCfAMVvbOL51TtObqV9ylc3P2L7CYMoVi+B74JKfQW1W0Z/D33jJvkXKuJ+mjwepgohOrrsWcxMali1yy9zv7+IC7qfXn5JkOEuAZava7/vJaX8Vlz7mKZs5zuhaRsZQZgccWsdm2bTG5ID4WF+CemZ5x73+nQKK93zOBc4Hj6+ZZs+YVWYvHe67IaJcCX3O6CCdIgGegObd+/cMFZ1XWufKL/E7XIjLbyHvgUXuyU+iHrUjv4O/dJW6n7n+3AL9LcP2i2K/6lpUeGX1nt7+l1n+e00WkmgR4BvGVV6rZ7675YsHyi+60PPn5TtcjMp7CvAdoAFvbk55Cb1KRoXZorqLJbSGbBvcEq4NjHk+2ocJjAe8ATnhdWOfMsXLuzT3HeIF7qPXn1BoHCfAM4SuvtAqWX/QV31mV31Euj2z/E0mih94DtD25feBhdE+30sN7wAtdTk2hJ9r7fRYwE+h5V4V7ZYFzi+xE6qwFvuV0EakkAZ4BfOWV3rxFa75ReM51f6/c3gnPUxRiEhTDI3Bg8ovYerA7idtC5vI5MgJ/MVgdfDPB9SuJ1XjlUtn7nUP+glr/eqeLSBUJ8DTnK6/0eeYs+1bx+VVfszx5Yx+sLMSZsRW4hjuxTW4feJuyQ0yyics0SrR4rQhYD7QESlXxEr86K3VlCYe5MEeP5sR7pQR4GvOVVxa4Zy78pv+SW79oeQsmPtZJiMlTmO5klhq8B25HbTWJ94TjKtpPfICnvolLN/CrBNfPw7yRR9+72rPOcm6Lm3DGSqDG6SJSQQI8TfnKK/Pc/rlf869//+esPJ/cvxPJp3WUuEVsk12F3mK6sA0tHrPyUj4CvzdYHewe60Ls4JJ3AO0KuGC+7P3OUX9FrT/rZ14kwNOQr7zS4yqe9Wf+yz7wJVd+kZN7bEX2Gto+NkjbUdtSE0+htyq7Z/BrXUWuPOVSqV6XkWjx2uLYR8cNK9yBkjw1I0U1ifSSB/yP00VMNwnwNOMrr3RbhaWfL73sg191+fylTtcjsppNXIjbkx+BDzdxKUt5E5ddwergCwmurwciANctl9F3jns7tf4POV3EdJIATyO+8krLyi/+eOllH/obV9GMMqfrEVlNgR5xsLbZRjbxe8IRFRkO8FJPqu9/J1q8lgdcDbSWFai8s2Zaa1JWlUhX/05t9g6EJMDThK+8Uimv74/8l3/oW+6SWbOdrkdkvcFFbMMj8ElOoR9Qw13YXCUp3QMeBu5OcP1szFnRA7etca91W0r6JYi5wD86XcR0kQBPA77ySqXcebeXXv7Bf/CUzpXe5iIVTglwrW090Sp0Gx05oqJ9g5+7C1PaRvX3wepgS4Lrb8esUOeShS7Z+y0GfYZaf6XTRUwHCXCH+corFZbrZv9lH/gXz8yFC52uR+QKBSbAh9h2xFaxC+PpQ3doFbeFzJfSFeiJps9nY44OPVm50DV3dqE1P3VliTRnAd+j1p912wklwJ339pILb/lH7+ylS50uROQaPXIV+iQ6sXUqu5u4PeCugpR1YTsMPJLgeiWxGYWbyqXzmjjFxcAHnC4i2STAHeQrr7yqoHz9N/KXnHuO07WInKPQp47AJ+rEdhK7D2e6sN0ZrA5Gx7qwocLjwkyfHy9w41oz21qboppEZvmnbOvQJgHuEF955aXeuSu+WnTOtTnTt1ekFaXMCHzEIraJ7oE3myYuQ33QU9SFTQM/SXC9AvADve9d7Vmd51bStVCMZSnwJaeLSCYJcAf4yivPtXylf1ZyyXvfpiyXrJQVThkxotV2ZMJtZMfNAjbzdS4slZpTvp4OVgf3Jbh+FdAHcPkS2fstEvprav2znC4iWSTAU8xXXrkQy/3F0is+fJnlLZAua8Ipg53Yhhb22Dpqqwm2kbXGNXHxlnmLVGr6jCdavFYCXAS0VpRZpQuL1fIU1CMylx/4W6eLSBYJ8BTylVcWA1/yr7/9Undx2SKn6xE5To+eQp94BH4sLsDdM1Kyhawd+HWC6xcQ2xL37lXudXJuiZiEz1DrX+l0EckgAZ4ivvJKN/CpwrOvXZ83r1wWrQmnjdELPTLhPfBDViQ0+HuPPyVtVH8erA72jXUh7uCSNkuhzpsn0+diUjzAPztdRDJIgKeAr7xSAe/JW7j6el/FZZc5XY8QDC9iGzKZEXhTfBe2opRsIUt0cEkAmAd03bzSvaLIqxy5JfXxB3uZ869dnPP94QPS7tsR5uzvd2N9q5NXj4y5eB6A777Yz9nf7+ac73fzwV+H6IuY/yRfe6yPc/+nm4/+ZuiPm3u2DfAfL/WP91Li9LybWv+lThcxVRLgqVHpKp79/uKLNlyplCV/5iJNjOyFbkcjCe+Bh9E93UoPjYZdhdPeRnVrsDr4eoLrVxA71vTqgNux0ffH1nl45CO+EY+dM8figfcV8LalrnG/7nCnzX++PMCrnypk++eLiNrwy+1hOvo0LxyK8sbniohqTbA5Sm9Yc+e2MJ+/2DvdP04u+TunC5gqCZNp5iuvDCh33mdLr/jg5ZY7T871FulisJXqEHuCKfQe7E7itpC5fNM+Ak+0eK0AE+Ct84uUb1mpqpjmWsb1tqVuZhaMvPe+eraLilnjh/egiA29EYjYmlAYFhRbWAoGohqtNb1h8LjgX18Y4E8v8eJxyT3+JLqeWv/lThcxFRLg08hXXlkKfMl/+QcvdflKpce5SC/69KbQ28054Klq4tIH/CzB9bWAFwjftsZzrstSE6dlmllYYvHV9V6WfLeL+d/pxp8PN6xwU5ynuHW1h/N/2MOyUgt/nuKVI1HetSrVx67nhIwehUuATxNfeaUX+HzRupvWe2ctcWx0IMR41Kkj8ISHmbSq6ADxAT69TVx+E6wOtiW4fj3QCXDRgsw8uKStV/Pg7gj7v1TEkT8vomcAfvqGmeD4y8vz2PrZIr7zjny+8VQ/f3d1Hv/3+gDvuy/E3z8j98GTpAV4KJN7pEuAT4PYorUP5i0658qC5Rde4nQ9QpxCn3oeuG1HEh4n2mK6sIUHP7fypnUEnmj6fD5wFtD2tqWuhTMKVEYev/v4vgjLSi1mF1p4XIr3rnbzwsGRC962HDWfryyzuHtbmHtv97G9JUrDifEXxokJNQNfAZZR2/Edajv0RF+QrqQL2PS42sorurH4/JsuS1GjCyHOxKhFbGEbxr/J2mqm0DWAq8iVp1xquuZ09wNPJrg+eHAJ71iRuQeXLPErXjocJRTWFLjhif1RLpo/8k7AN57q50e35BO2IRqLGUtBKDzGC4qEegZ06IWD0c0XLnDdOvOfO3sn/or0JwGeZL7yygrgo/717zvX8uZLpzWRrpQaeQucqB22LVyJRuBDTVw8ZdO6B/yOYHVwzFHRhgqPB3NwSUtJHp6KWZbjPRU++OsQTzdGOR7SLPr3Lr51dR4zCxRffLiP1pCm6uch1s2zePQjhRzpsvnkpj4e+rCPykVublvt5oIf9uC24Pz5Lj594fC/iX67K8zFC1wsKDb/SdYvcrH2f7o5d67FefMy7pa/Y0Jh3fVMU/T5O7cOvBYKsxhYsemf2e50XckgAZ5EvvLK2cAXC1dfNddTtmiN0/UIkcCpU+jRsK3IHzfAj6jIcICXeqbr/rcN3Jng+iqgEDh+62rPOq9LOb6v6he3+sZ8/D2rT52gWFBs8dCHh5//rWvy+dY1Y7/uu1d5ePeq4c//7YZ8/m1KleYWE9yR5+7cGn49FMYDLAS6iPXNzwYS4EkS67T2Gbd/rt9Xcfm1TtcjxAROCfCoHU64D/xAfBOXkmnbA/6HYHXwYILr1wIhgEsXSec1caqeAd25uSny3F1bw6/3RvBigrsb+Cnw/KbdYQlwcYobUaq85NLb36Zc7qw6c1ZkJ2W2kQ0fZhIdGHcbmY2OHDEnkQHgLpy2PuiJFq/NAM4DDp031yqbX2wtmaYaRAbqHtCdzwwHdx6wCBPc9wAvZFNwD5IATwJfeeUK4LbiCzcscRfNlDcVkSGGbzNrrdHaHncbWR+6Q6u4LWS+aVmBfhzYlOD6RbFf7VsqnOu8JtJL94DueLox8tzd28Jb+kxwL8ZsMbwbeDEbg3uQBPgU+corfcBnvHOWu/MXr73S6XqEmCSldPwqdDOdPl6Adyq7m7g94K6CaenCdk+wOjgw1oUNFR4LuAE44bGw1s5xrZuG7y8ySPeA7nhqf+TZe94Ib+0bHnF3AncAL23aHc76DfMS4FMQ2+/9PizXrOILN1yrLEuWhoqMoIcPM1EAWtsJA/wkdh8ju7BNR4AnOrhkBTALaNpQ4a4o8ChpS5yjuvp1+1ONkWfv3hbeOhClgJHBXZ8LwT1IAnxqzgOuLblww2KXr2S+08UIcRoUcaeRaa1tC8ZtWxBr4jI0Op6GJi71wergjgTXrxj8/lcFMnfvtzhzXf26/cn9kWfueSO8LRbci4EOhkfcY87eZDMJ8DPkK6+cAXzaO3dFNG/R2TJ1LjKOiu+Frm3bnWAFeqtZwGbaf7mwVPJHwIkWrxUClwHNS/2qaIlflSf5e4s01tmv257cH3n2nm3hbWF7KLjbgZ9gRtw5F9yDJMDPQGzq/CNYLm/xBTe/Q6bORWayh6fQ0bYncYAP7QH3lnmLktxhsAf4ZYLr52HeqyLvXe1ZZ0l3w5zQ2a9PPrEv8uxP3wi/EbbxAUuAk8D/Aa/kcnAPkgA/MxcDF5dcuGGRy+eXqXORkZQmfgpduxOcjXAsLsDdM5K+hey+YHWwa6wLGyo8CngHZsTFBfNl73e26+jTJx/fF3nmZ8HwGxGbQoaD+0eY4JZGsjES4KcpNnX+x57ZgYG8RWe/zel6hDhzcd1KJ5hCP2RFQoO/9/iT3kY10eK1hZg38Kbrl7uW+vPVzCR/b5EmOvr0icf2RZ75eTAcjAX3UuAE8EPgVQnuU0mAn4bY1PmHAXfxundeLVPnIpMpE+CxVeg6YYA3xXdhK0rqFrLdwergcwmuX0bs3vvbl8ve72zU3qePP/ZW5JlfbA9vj9gUYYL7OPAD4DUJ7vFJgJ+ei4FLCsovdbtLZq9wuhghpkRrRWwYrrFtV1xXtnhhdE+30kPNMFyFSW2j+pPxLmyo8HiBa4CWmQUqr7zMkvMFskh7nz7+6N7I5l9sD++w9VBwt2KC+9VNu8MRZytMfxLgk+QrrywFPoblbilcefnHHC5HiCTQw/+TYATeg91J3BYyly9pI/AIcFeC62cD+cDAbWvcF7qtaTu+VKRQW69u/YMZcccHdwvwfeB1Ce7JkwCfvA8A7uJ1N55j5ReWOV2MEFOl0BaDN8IT3ANvN+eAxzdxSdYIvC5YHWxOcP3tmBXqVC50yd7vDNfWq1sf2RvZ/Ksd4Z22ppjh4P5vYIsE9+mTAJ8EX3llOXCpq3BGS/6StX/kdD1CJEX8FLq2bRdjB3irig4QH+B5SevClmjv92xgDXDg4gXWnNmF1oIkfU+RYid7dcvDDeHN9+6I7NRQggnuZuC/gK0S3GdOAnwCvvJKF/ARoLP4gqqrlcuT73RNQiRD/AjcTKGPvY0s1oVtaCFRkrqwHQUeSnD9EszZ4LpqpUdG3xnoZK/d/HBDZPO9OyJvavADAeAY8D1McEcdLTALSIBP7BJgqXfuWT2e2csumvDZQmQKHRfg2LZrnCn0VjOFrgFcRa485UrKvei7gtXBMd/AN1R4XJjp8+P5blxrZlvnJuH7iRQ5EbKPPdQQ2Xz/zsiuuOA+CvwnsE2CO3kkwBOInTT2QaCl6Nwbbk1y9ykhHKUYnkJH2/Z4jVxa4pq4eMqStgd83NXnwEqgFGh6zyrP2fluVZCk7ymm0fGQfaxuT+TpB96M7I4L7iOY87gluKeBBHhiNwBFvpWXzXKXzFrudDFCJNfIKXRrnBH4ERUZDvBSTzLufz8TrA42JLj+NmL33K9YIp3X0t3xkH20bk9k86/fjOzG/MNrGXAIcx73GxLc00cCfBy+8so5wC243M2+les/7XQ9QiSZVpNcxHZQRYb3gJckZQ94osVrJZh+C0fLZ1r+hSVK/uGcplp77CO/3xPZ/JtdkT2Y4A5ggvtOICjBPf0kwMcQ67h2GxApXvfOC6y8QmnfKLKKAg2jAnyMRWw2OnpERYe6sLkLp9wHvQO4P8H18zFT+dH3rHbLwSVpqKXHPvy73ZHND+6ONAAzMMF9EHOsZ3DT7rDtZH25RAJ8bOVApatwZnP+4nOucroYIaaBHrGITdu2e4wReB+6w1ZxW8h8U95C9otgdTA01oXYwSU3AG2WQq2bJ9Pn6aSlxz68aXfk6U27I3sZDu4DmBmV7RLcqScBPkrctrGO4guqrlEuT57TNQkxHZRpnTp4D1xbY4zAO5XdRdwecFfBlKfQEx1cshRYADS98yz38iKv8k/xe4kkaO62D23aHXn6d3sibzEc3E1IcDtOAvxUlwBLvHOWd3tmBy50uhghps/E98BPYvcR10bVyp/SCPyNYHXw1QTXL8e0V+XaZdJ5zWnHuu2Dm3ZHNv/eBPdMTHDvx5zHvUOC23kS4HFi28Y+BLT41lx1g2wbE1lMx3diY5x74LEmLvFd2KYyAk+0eC0fuBJomVuoCpbPsFZN4fuIKTjaZR94cHdk80MNkX2MDO4fATsluNOHBPhINwKF7hkLIp6ZC9c6XYwQ00jH7QNXsRH4Kf9gbVXRPmLHeeLCUh5VeIbfrx/4aYLra4E8IHzrGs8FLkvJUb0pdrTLbvrNrsjmR/ZG9gNlmODehzmPe+em3WGd6OtF6kmAx8S2jVUBRwrPvuZ6paxxz0YWIgvouH3garxV6K1xTVy8Zd6iKcxK/TZYHTyZ4PrbgS6AS+TgkpQ60mU3/XZX5OlH9kYaGQ7ut4D/Ad50MrgDNXUewNe4sarDqRrSmQT4sHcBEVfRTK93dkBWv4qsp0atQh/rHvixuAB3z5jSFrJE0+fzMN3XDly5xLVgZoGaM4XvIybpcKfd+Jtd4af/8Fa0ieHg3os51nOXw8HtBT4O/BWwCfiiU7WkMwlwwFdeORdYDxwqPPvaq5TlknOHRXZTZuRNXICPtQr9kBUZ2vLl8Z9xG9Um4PEE14cOLnnHWW4ZfU+zw532/l+/GX768X3RA8AsTHA3kB7BnQd8AqgBFsce/mSgpu7vGzdWJTp6NidJgBtvB6Iqr9CdN6/8EqeLESI19FBga23bFqfed25SkaEmLq4i15mOwO8IVgfHDIUNFR43cD3QWuzFs2qWdc4Zfg8xgUOd9r5f7wxvfmL/iODeDfwG2J0Gwf1JTHAvGnU5H/hz4Guprivd5XyA+8orZwDXAMeKzrnuYuWW40JFTjilkYsCb/wTwuiebqWH26gWntEecBvToWs8q4Ai4MR7V3vO87qU9F1IsoMd9r77d4affqoxehCYzXBwPwDscTi484FPYcJ5YYKnfi5QU7excWNVW2oqyww5H+DAVYBSLo/OW7hqvdPFCJEqyrQsjQV41GZUgPdgdxK3B9zlO6MR+OPB6uCBBNevAXoBLlssndeS6UCH/db9O8NPP90YPcRwcO/CBHdDGgT3ZzDBPX8SX1IM/CnwremsK9PkdID7yisLgXcCzYVnX3Ou5clPxklLQmSC+FXogyPwEWs/2s054MN7wPPPaA94osVrpcA64PA5c6yZ84utpWfw+mKUpnZ77307w08/0xQ9wnBw78RMle91OLgLMMH9l0wuuOP9aaCm7juNG6u6k19ZZsrpAMd0fvKgVDh/ydrLnS5GiBQaPMzEBtC2bTMqwFtVdICRTVxO9x+4J4DfJrh+IWYhnb2hwi2j7ylqarcb7tsZ3hwX3EuBHZj/Bk4Htw/4LPAXwLwzfJmZwOeAf01WXZkuZwPcV16ZB2wAWnyrrlxt5RWWOV2TEKmkNBax1qW2jtqWYsT951gXtvDg52fQhe2nwergwFgXNlR4LOAdwEm3hTp3rmvdab62iGlst/fctyO8+dkD0aMMB/d2THC/lQbB/Xngq8DcJLzkFwM1df/euLFKjiolhwMcs3WlEDheEDhfRt8i14w4ThSttYuRPVqOmyl0DeAqcuUplzrd7ZWJDi5Zjgmbpg0V7pU+jyo6zdfOefvb7N337ghvfv5g9BgwBxPcQUxw73M4uAsZDu5k7utfjLnt+fskvmbGyskA95VXuoF3A8cLll+03OXzL3C4JCEcMHwPPDYCH5HgLXFNXDwzPac7ff5KsDoYTHD9CmIL5K5aKnu/T8f+Nnv3L7eHn37xULSZ4eB+AxPc+x0O7iLgC8BXMP9Amw6fRgIcyNEAB87D3E9pKjjrEhl9i1yklY5v5BK1rVGd2I6oyHCAzzjtJi6JFq/5MOtPmpf4VdHSUlV+mq+dc7TW7G/Xu365Pbz5JRPcczHBvRV4EGh0OLiLgT/B7NeeNc3f7qZATd2ixo1Vh6b5+6S9nAtwX3mlBbwHaPfMWT7LXTxrudM1CeGAkavQbdu2GNmJ7YCKDO8BLz6tLWQh4BcJrp+Lee+JvHe15zxLndrCVRhaa/a16Td/uT28uf5wtIXhEfcW4MFNu8ONTtYXC+4vYoI7VeuIXJhubTm/pSznAhxYjen00+hbcfHbnS5GCOcM3wO37QhupYYWBtno6BEVHerC5i5yn84I/P5gdbBzrAsbKjwKs3itA+CC+bL3eyxaa95q0zt/ERzY/MoRu5Xh4H4d2JQGwV2C2Zf9Z5jZzFT7RKCm7tuNG6ty+mjTnApwX3mlwtz77kJZyjN76bkOlySEQzTENXKx7bDlVWpoy1gfusNWcVvIfKe1hSzR4rUFmH3JTdctcy0pzVey+yOOrbV+66S98xfbw8+8aoJ7LrCE4eBucrK+QE2dn+HgnuFgKYOL2eocrMFxORXgmJWvZwFNBSsuWiGNW0QOs+M7sUWjYSs/LsA7ld1F3B5wV8Gk26g2BKuDzyS4vp7Y+eLXr5C934NsrfXek/aOnwfDz7x+1D7OcHC/hgnuRN3spl2gpq4U+BLwZaDUyVrifAYJ8JxyFbGVr/mL165zthQhHKUx9xIHp9BdeZY1NGV+EruPuDaqVv6kR+A/Ge/ChgqPF7gWaJ2Rj7d8pnX2mRSeTQaD+2dvhDdvOWafwDQ5WQK8ignug07WFwvuP8OEt9/JWsZwU6CmbmHjxqrDThfilJwJcF95pQ/zr/9mq6A4z106b5XTNQnhGI3NiAAfOQKPNXGJ78I2mRF4FLgrwfU1QAHQcusazwWe099XnjVsrXXDCXv7z4LhZ7Yes08yPOJ+Gfh9GgT3DExw/ynpF9yDBhez/Z3ThTglZwIcWEts5atv5eXnKcuVSz+7EKON2HKkbdv2xC1ia1XRPmJT3biwlEcVTuI1HwpWB48muH4d0ANw6SJXTu79trXWe07YwZ++EX7mjWa7jeHgfgkT3I5ujQrU1M3ErCj/InCm57+n0idiZ4Xn5GK2XAqxtwOdAHnzV65zthQh0sJQiGsdjY4K8KE94N4yb5FSSo3+4jEk2vtdBpwNHLxwvjV7TqGV6OjIrGNrrXcft9+4543wM9tb7HZMcC8GXgTqNu0OOzoNHKipK8M0X/kTzMlfmWIJObyYLScC3FdeORezeO2AZ/ayMldh6egD44XINSNG4LYd1R6lhkYxx+IC3D3DPZk39GMkfhO9ePD73rzSkzOjb1tre9dx+417toWf3dE6FNyLMCPudAjuWQwHd6a2s/00EuBZ7WLMqUvad9Yl6xyuRYh0oIkL8dg+8KHPD1mR0ODvPf5JdWG7O1gdjIx1IXZwyfXA8Xw3rrPnWFm/fdPW2n6z1Yy4d7baHZjFaYuBFzDBfcTJ+gI1dbMxfco/T+YG96CqQE3dgsaNVY7+mToh6wPcV17pwtx7O4FSyjN76XlO1ySE8/Tw9DlYoKPxbwZNKjK0It1VNKkubOOuPgfKibUufleFe02+W/lOu9wMEbW1/eZxe9s928LPvnl8KLiLgeeBhzbtDidaIzDtAjV1cxgO7smsa8gEg4vZvu10IamW9QEOrMDsW2wqWH6x7P0WwhieQte2B1S3UsoNEEb3dCs93Ea1cMI94M8Fq4O7E1y/itiK9iuXZufe76it7Z2t9ta7t4Wf3X3C7sIEdwnwLCa4jzlZX6Cmbi7mLO7PAdn4D6gPIgGelS5ncO/3Etn7LUTM0BS6Rnsty9VGrJd1D3YncXvAXb4JR+CJFq8VA5XAkbNmWiWLStSKqRaeTmLBveWubeFn95ywuzH3uAeD++E0CO55wF8Cn8Vs4ctWqwM1deWNG6sanC4klbI6wGN7vy9D9n4LMZLZB25+q22PZbmPYcKHdmWHiN8Dnp9wD3gXcF+C6+djOr5F373Kvc6a3Gr2tBe1dXRHq73lrq3h5xpO2t2YEbcf2Aw8sml3uNnJ+gI1dfMxwf0Zsju4470L+Deni0ilrA5w4ByG9n5fJnu/hRg2vIhNa6/bnX8Ccy+R48ruZ2QTl0Qj8F8Gq4M9Y12IHVxyA9CmgPPnZf7BJVFbR4Mt9ut3bws/t/ek3cPI4H540+5wi5P1BWrqFgBfw6zMzneyFgdIgGeZob3f3rkrcr5toxBj0WiXy53fRuz9INaFLTx4fYIAT3RwyRJgIdB041nuZcV5qjQJ5TpiMLjv2jrw3FttugeYz8gRt9PBvRCoAT5J7gX3oMsCNXWzGzdWtTpdSKpkbYDH9n6XAwesguI8V9HMpU7XJES6UAxPoSuU7XbndRJ7P4g1cdEArkJXnnIp7zgvsyNYHaxP8G0uI/YPgeuWZ2bntYitI8Fm+/U7tw48t79dhxgecT8FPLppd9jRsAjU1C1iOLjznKwlDVjAzcAdTheSKlkb4MBFxKYJCwIXnKWUZTldkBDpQ8c3ctEeT2EXwyPwoSlxT5nnjEbfGyo8+ZjV561zClX+8hlWRq0/idg68kaz/dqdWweeb2zXvZjgLgWexAT3cSfrC9TULQb+Cvg4Etzx3oUEeGbzlVdamL3fxwG8c5evdLYiIdJOfICr/IIZHcTeD46oyHCAzxi3icsAcE+C1z8HEyzhW1e7z3dbKiPeayK2jmw7Zr9659aB55s6dB/Dwf048FgaBPcS4K+BPwbGmxnJZdcHauoKGjdW9U781MyXEf+nOgOLMdNcB1GWcvvnljtdkBDpJW4bONoqKp4/FOAHVGR4D3jxuFvIHgxWBxOF2dsxK9S5ZGH6L16L2Dq81QT3CwdMcM/H/CE9Afxh0+7wCSfrC9TULcUE98eQ4E7Eh+n6t8npQlIhWwN8aMFa/pK1i5XbmyvbKISYrMEEdytl9Rb4yvoAt42OHlHRodGLu8g93gg80fT5XKACOHDZYtf8Mp81L2lVJ1nE1uEtR+1X7tw68MLBTt2PGXEDPIYJ7pMOlkegpm4ZJrirgZw9fvU0vQsJ8Iy2HmgHyFuwSqbPhTjVYIDnWZa7A4gArj50h63itpD5xlyBfgATcOO5hNjZAzeelZ6d18JRHd5yzAT3IRPcgyPuxzBT5ekQ3F8HPkr2vk9Pl5sDNXVWLhwxmnV/MXzllTMxp/00AbhnLpQAF2J8eS6X9yQmwN2dyu4ibg+4q2DMNqp3BquDY745bqjwuDFTmK0+D+7Vs6y101H0mQpH9cDrR6Ov3LE1/MKRLh3GjLg18Ajw+Kbd4TYn6wvU1K0A/gb4I7Lw/TlF5mAGcc87Xch0y8a/IBWDv3GXzitx5RfNdrIYIdKRihuBu935QwF+EruPuDaqVv4pI3BN4lW+FZjDO07etsZzbp5bpcWe5HBUD7x2NPryHVvCLx7tHgpuG3gYE9ztTtYXqKk7CzPi/jDZ+b6cau9CAjwjVQLdAPmL1y53uBYh0pQeHEF7PN7Ck0BUo92xLWTxXdhGj8CfCFYHGxO88DVAL8Bli51fvDYQ1QOvHYnW37E1/OKxbh1hOLgfAp5Ig+AuB74BfIhYJzyRFO/CtJLNalkV4L7yynzM9pUjAJ5ZS7Lq4AQhkmhoGXpsD3gE8LSqaB8QBcCFpTxq9JGTiRavlWJ6nx9eM9uaMb9IBZJc86QNRHX/q0ei9XdsCb/U3DMU3FFMcD++aXe4w6naAAI1dRWYEfcHkeCeDisDNXWrGjdW7XK6kOmUVQGOOTrUItZlyl0yW0bgQiRme/OKeoCINgE+tAfcW+YtUiMPHzkJ/CbBa10IKMB+V4X7fCfOLRmI6v5XDpsRd0uPjjIc3L8HnkyD4F6FGXF/APNeJabPOwEJ8AxyDrHRQ96CVfOU25uN594KkQTDndgKCsq6iQX4sbgAd89wj77//bNgdbCfMcQOLrkeOOm2UOfNc62bhqLHNRDV/S8fjr70ky3hl46HtI0J7jDwO0xwd6ayntECNXWrMcH9fiS4U+Uy4LtOFzGdsibAfeWVCtM+tQ3Au6BCps+FGN9ggFtFxQsGR+CuQ1YkNPgEj/+ULmyJDi5ZjjmOtOnmle5yn0dNdIZ4UvRHdN9gcJ/o1Zrh4H4QeCoNgvtsTHDfjgR3qq13uoDpljUBDpTFPg4AeGYulOlzIcahNBpz7zVaOmNZiNg+8PgubJbPKov7kteC1cFtCV7ycmIHl1wdcE/7wSX9Ed1Xfzj64h1bwvWjgvs3wNObdoe7pruGRAI1decA3wRuw9xWEKm3MFBTt6hxY9UhpwuZLtkU4EOBrVwel6twxhInixEivWmN6VV+1O3Jt4FoBAa6lB7qwmblW/FbMBMtXvMBVwAti0pU4VK/mrbeC/0R3fvSoeiLd2wNv3yyV4MZ9Q+QPsG9FhPctyLBnQ7WA/c5XcR0yaYAPx/oA/DOO2uOslzZ9LMJkWyDAX4csL/wg2v1c6vsHob3gFsun2twBXov8PMEr7UW0+YzcutqzyUuSyV9qrhvMLi3DNS39aEYDu4HMMHdnezveToCNXXnYYL7PUhwpxMJ8HTnK690Aeswq2TxzFqywNGChMgM+ZgAjwB0KruP4T3gs9x+9+Be8V8Hq4Njrt6OLV57B9ABcOGC5O797ovo0IsHoy/+ZMvAyx39KMxUeT/wa2BzGgT3OuBvMfuOJbjTT1bfB8+KAAcWYk7oCQO4/XPnO1uOEOlOa8z/Z5qByJurVqsTlh1mOMAL3IXuwcMzEi1emw8sA5quCbgWl+arWcmori+iQy8cjL5wx5aBV0YF9/2Y4O5J/ArTK1BTdz4muDcgwZ3OLgjU1OU1bqwac/dEpsuWAF9O3ApPV+EMGYELMTEbM2sVqXP1eBUqjPlHcD7Qg4tZwFvA5gSvsZ7Y1s0bVkz94JLesO554WD0hTu2DrzS2Y+L4eC+F3g2DYL7Qkxw3+JkHWLSvMAFwItOFzIdsiXAVwEhMAvYrPziOQ7XI0Rai/VCHwA6gUi91e9foT09mMdnu2e4H1dKfQH4p2B1UI/1GhsqPB5M69RWfx7elWXW2WM9bzJ6w7rn+YPR5+/YMvBq1wAuzMi+DxPcz2zaHQ4lfoXpFaipuwioBaqcrEOckfVIgKe1cqALBhewWdKaUIiENJgA7wIiPUrPbiHag5kOtkrXlx7CjKzvTPAia4BC4Phtazzne1zKe7pVhMK6+/kD0efv2DrwavcAbmABZtHcrzAjbqeD+xLMiPsmJ+sQU5K198EzPsB95ZVFwAwG93+XLZH730JMxIypw5gReNRGlx5RkR6gFNhbuLIwD3gkWB08kuBVriE283XpItdp7f0OhXX3cweiz985HNwLgR7MavfnNu0O9yZ+hekVqKm7FBPcNzpZh0gKCfA0toC4gxnc/jly/1uIxPSoEbiyofSQaeJSAvwMWE3ivd8zgXOBg+fPs2bNLbIWTeYbh8K665mm6PN3bh14LRTGw3Bw/wx4Pg2Cez1mqvwGJ+sQSZW1DV2yJcCHVoG6imbKCFyICcT+D9P9VFfXwH9/9klXt7JLjqhoBBPqQcze7t8neImLY7/qm1d6Jhx99wzormeaIs/dtS38+qjg/inwQhoE9+WYEff1TtYhpk1W7gfPhgBfRez8YVxuy8ovnutsOUKkP20y/ATAF35wbfQb68KFtsIPPNq5pbN37V1rnwhWB8Njfe2GCo+FGaEe97qw1s61zhvv+/QM6M7NTZHn7toafr03ghcT3N3APZjg7hvva1MhUFN3BWbEfZ2TdYhpJwGeps7CvCGQN1cWsAkxGcpMoR8f/DyimIW5J/48QLA62Jjgy88CZgJN717lXp3vVqec+tc9oDs3N0aevXtbeEtvhDxgEekV3G/DjLivdbIOkTJZeR88owM8toCtDGgC6cAmxCRpNApojXusDNgDNE7i668i1nL1yiUj9353D+iOpxsjz929LbylzwT3YsxCubsxwe1oQ41ATd3VmOC+2sk6RMqdG6ipU40bq8bcEpmpMjrAMXtFB9s9Sgc2ISZNA7QAlJxfooAi4NedWzoTvsFtqPAUAZXA0WWlqnixX50FJrif2h959p43wlv7hkfcncAdwEtpENzXYIL7KifrEI7xYf5OHnS6kGTK9AAfvYBNRuBCTMDW0UfR0ReAnbGHvJhe5q9P4svXETuG9L2rPet6Buh4qjH87N3bwlsHohRgRtwdmOCuT4Pgvg4T3Fc6WYdICxVIgKeVCmInkMkCNiEm56XG4FfiP+/c0tlfcn5JbeeWzkiir4s7uKQNYEerfeg/6weeD9sjgvvHmOAeGP+Vpl+gpu56THBf7mQdIq2sBB53uohkyvQAH+7ANmtpmSxgE+LMTBTeMYsx05BNAI/sjbTEHmsHfkJ6BPcNmOC+zMk6RFqqcLqAZMvYAPeVVxZiFt4cAHAVz/I7W5EQWe8yzNGjRcAszEEo/we8kgbBfSMmuC91sg6R1iTA08h84jqwuQpLJcCFmCYbKjx5mC1XhZjFaT/CBPeYe8VTJVBTdxPwTczCOiESkQBPIwuJO0LUKiiRABdi+pQAh4DHgFfTILirMCPuiyd6rhAxSwI1dQWNG6sc7fqXTJkc4AEGF7ABrvwiCXAhpsmm3eHWDRWeb2/aHXZ0H22gpu4WzIj7IifrEBnJApYxvPsi42VygM8lLsBVXqEEuBDTyMnwDtTUbcAE94VO1SCyQgAJ8LQwBxjaY2p5CyTAhcgigZo6BbwLE9znT/B0ISYj4HQByZSRAe4rr7QwZ4APHQ+n3HnFzlUkhEiWWHC/BxPc4x6UIsQZCDhdQDJlZIADg2GtAVwlc4pkD7gQmS0W3O/FBPe5DpcjslPA6QKSKVMD3E/cFjK3f45MnwuRoWLBfRvwDcw55EJMl4DTBSRTJgd4fA90CXAhMkygps4CbscE99kOlyNyQ8DpApIpkwN8aA+4y+eXABciQ8SC+32Y4F7jcDkit8wO1NT5GjdWhZwuJBkyNcBnY1o6AmDlF0uAC5HmYsH9AeDrwGqHyxG5azaxfv6ZLlMDfB7xW8jyZQ+4EOkqUFPnAj6ICe6sa2cpMk6J0wUkS6YG+BzimrhYXp8EuBBpJhbcH8IE90qHyxFiUNbkRaYG+GzM2cOANHERIp3EgvsjwN9gjvwVIp3ICNwpvvJKL+ADjg896PL4HCtICAFAoKbODfwRJrhXOFyOEOPJmgFfxgU45g/fHvxEeQs8SqkETxdCTKdYcH8UE9zLHS5HiInICNxBI/71ZHnyM/FnECLjBWrqPEA18NeYU56EyAQyAnfQiCYuypPncbAWIXJOLLg/hgnugKPFCHH6ZATuoELimrgod14m/gxCZJxATZ0X+GPgr4ClDpcjxJmSEbiDRoy4ZQQuxPSKBfcngBpgicPlCDFVMgJ3UD7xi9jc3kz8GYRIe4GaujyGg3uxw+UIkSwyAndQHiMDXEbgQiRRLLg/hQnuhQ6XI0SyyQjcQflAdPAT5fJk4s8gRNoJ1NTlY4L7a0hwi+wlI3AHjRyBS4ALMSWBmroC4NOY4J7vcDlCTDcZgTto1BS6R6bQhTgDseD+LPCXmAOChMgFMgJ3kIzAhZiCQE2dj+HgnutwOUKkmgS4g/IYcQ/cLSNwISYhFtyfB/4Cc6KfELnImvgpmSETA9xL3AgcGYELkVCgpq4Q+ALwVcxJfkLksgGnC0iWTAy/kVPolozAhRhHUaCm7muY4J7ldDFCpAkJcAeNGIErlzsTfwYhUuFBzP9fhBDD+p0uIFky8V6Al7h74CiViT+DEKkg4S3EqbJmBJ6J4TdiBK7taDTBc4UQQoh4EuAOGrmIzY5GnCtFCCFEhpEAd4KvvNLC3LcfHoFHI2HnKhJCCJFhJMAdokY/oKMRGYELIYSYLAlwh9iAJj7IbQlwIYQQkyYB7oRQQ70GwsTVraNhCXAhhBCTJQHuoAFGBLiMwIUQQkyaBLiDRgZ4pD9r/mMIIYSYdtLIxUEjp9AHerPmP4YQQohplzWDvkwM8BEjcLuvp8/BWoQQQmQWCXAHjQzw/h4ZgQshhJgsCXAH9QGuwU+ivV0yAhdCCDFZJ50uIFkyMcBDxJ2ipgdCYa1tO8HzhRBCiEGHnS4gWTIxwLuIG4EDEI3KNLoQQojJkAB3UDejzjHX0bBMowshhJgMCXAHdXFqgPc6VIsQQojMIgHuoFPC2u7vaXegDiGEEJnniNMFJEsmBng/8eeBA3Zfd7szpQghhMggxxs3VmXNmqlMDPA+zIlkQ6KhjjaHahFCCJE5smb6HDIzwEOjH4h2n2x3oA4hhBCZJWumzyEzA7yd+PPAgUhna7sjlQghhMgkMgJ3WCcmwIdCPNJ2uF1rPf5XCCGEEBLgzgo11EeANiBv8DEd7o/oyEC3c1UJIYTIABLgaaAZyI9/QA+E2p0pRQghRIaQAE8DhxkV4HZ/j6xEF0IIkYgEeBo4StwUOkC0t6vdmVKEEEJkCAnwNNDG6GYushdcCCHE+PobN1Ydd7qIZMrkAB/ZzEX2ggshhBjfXqcLSLZMDfB2Ru8F72hud6QSIYQQmWC70wUkW6YGeGfs16H6w21HOrRsBhdCCDG2oNMFJFtGBniood4GjhO/kM2O2jrc1znuFwkhhMhlMgJPI8cYvZWsr+eEQ7UIIYRIbxLgaeQoowI82nU8q7YICCGESIoeYJ/TRSRbJgf4EcAT/0C47bAEuBBCiNF2Nm6syro1Upkc4O2M2krWf3SPBLgQQojRsm4BG2R2gJ9g9F7wztZuWxayCSGEGGmL0wVMh0wO8GZMgI/4GaI9bVl1YLsQQogpe9XpAqZDxgZ47FjRRqA4/vFoR6tMowshhBgUAbY5XcR0yNgAj9nJqAAPnzwoAS6EEGLQzsaNVb1OFzEdMj3A9zOqpWr/kT1HpCObEEKImKycPofMD/DDjApwu6+rX/dLQxchhBAAvOZ0AdMl0wP8ONDHqP3gke6TMo0uhBACZASenkIN9RpoYNR98EhHswS4EEKIAbJ0ARtkeIDH7AAK4x8IH2+SABdCCPFi48aqfqeLmC7ZEOAHGdXQZeBoQ7O27ahD9QghhEgPf3C6gOmUDQF+mFE/h46Go3Zf5zGH6hFCCJEeHnO6gOmUDQHeBbQx6mSySNcJmUYXQojcdYIsXoEOWRDgsYVsuxnd0KW1ab8zFQkhhEgDTzRurLKdLmI6ZXyAx7wJFMQ/0Nu4ZZ/cBxdCiJyV1fe/IXsC/DAw4l9aur9nINp9otGZcoQQQjhMAjxDHMH8LCO6soWPH9jjTDlCCCEctKtxY9VBp4uYblkR4KGG+j5gH1AS/3hf0zYJcCGEyD1ZP/qGLAnwmBcBf/wD4ZOH2qN93a0O1SOEEMIZEuAZZtdYD0ZOHpZRuBBC5I4B4Gmni0iFbArwI0Ano/aD9x/d0+BMOUIIIRzwQuPGqh6ni0iFrAnw2H7wF4GZ8Y/3NW07oCPhPmeqEkIIkWJZ3X0tXtYEeMwbgGvEI9rWkc6Wvc6UI4QQIsVy4v43ZF+A78PsBx8R4gMt++Q+uBBCZL/jwOtOF5EqWRXgoYb6fiAIzIh/vHf/6w1aaz32VwkhhMgSD2V7+9R4WRXgMS8DvvgH7FBHX7SnLes39QshRI67x+kCUikbA3wPozqyAYRPHJRpdCGEyF5HgCedLiKVsi7AQw31J4FDjDqdrP/gdglwIYTIXj/PpelzyMIAj3kRKI1/YKD5rVa7v+eEM+UIIYSYZj91uoBUy9YA3znWgwMt+99IdSFCCCGmXbBxY9U2p4tItWwN8INAL+CNfzDU8NI2WYwuhBBZJ6cWrw3KygAPNdRHgVeAsvjHI21HOqLdJ/c7U5UQQohpYAM/d7oIJ2RlgMe8DnhGP9h/ZFfOTbMIIUQWe6pxY9Vhp4twQjYH+C6gj9HT6Hte3KmjkQFnShJCCJFkOTl9Dlkc4KGG+jCwGZgd/7geCIXDbUfedKYqIYQQSRQCHnC6CKdkbYDHvMTow02AvgNvbE19KUIIIZLswcaNVV1OF+GUbA/wA0Azo5q69O1/vdHuD7U5U5IQQogkydnpc8jyAI+dEf44ow43Aeg/1vBa6isSQgiRJC3k0NGhY8nqAI8ZPFpuRH/00K7ntmjbjjpQjxBCiKn7RePGqpx+D8/6AA811LcB2xm1JzzafSIUaT8qi9mEECIz/cTpApyW9QEe8wRQOPrB3sYtMo0uhBCZ57HGjVU53xo7VwJ8J9AN5Mc/2Lf/9Ua7Tw44EUKIDPOvTheQDnIiwGN7wv/AqD3hAP3H9sgoXAghMse2xo1VjzldRDrIiQCPeQnz845YzNbz5rNbtR0JO1OSEEKI0/RvTheQLnImwEMN9a1AEJgV/7gdau8daN7/qjNVCSGEOA0HgV86XUS6yJkAj3kc8I1+sHv7Ey9oOxpxoB4hhBCT9/8aN1bJe3VMrgX4TqALKIh/MNrZ0h0+3rTFmZKEEEJMQgfwv04XkU5yKsBDDfUR4FHGWMzWvf2p57W27dRXJYQQYhJ+mMt9z8eSUwEe8wwQYdRZ4ZG2wx3hE4dyfl+hEEKkoQHgP5wuIt3kXICHGuq7gEeAeaOv9ex46lmttU59VUIIIRL4eePGqiNOF5Fuci7AY54ENOCOfzB8vOlkpO3IDmdKEkIIMQ7ZOjaGnAzwUEN9O2ZF+qmj8Dc3PyuDcCGESBsPN26skoHVGHIywGMewzR1ccU/OHBsb0uko3mXMyUJIYQYRdqmjiNnAzzUUH8C2MwYo/DQrmefSX1FQgghRnmtcWPVU04Xka5yNsBjHsXcBx/x59B/+M2jkc7Wt5wpSQghRMzfOl1AOsvpAA811DcDzwFzT7m254XNqa9ICCFEzObGjVV1TheRznI6wGMeAfIY9WfR17TtYKT7ZJMzJQkhRM77mtMFpLucD/BQQ/1h4GVgzuhrvQ0vyShcCCFS79eNG6vqnS4i3eV8gMfUAfmMOmq0d9+r+yMdLXudKUkIIXKP1joC/LXTdWQCCXAg1FDfBGxljFF417ZHHtG2HU15UUIIkYOUUv/XuLFqj9N1ZAIJ8GG/Y9QpZQDh1sYTA817ZSpHCCGmmda6B/iW03VkCvfET8kZ+zDHjQaAlvgLXa//frPnhi+ca3nyipwoTEDnK7+le9sfQIFndoBZN32Z9md/SmjvyyiXG3fpPGbd9GWs/JH/iXRkgGM//xo6EgbbxldxOaVXfhiAtqfvoHffa3jnLGPWzV8BoHv7k9h9XZRc9K6U/4xC5Dql1L80bqw65nQdmUJG4DGhhnoN3Af4GPXnYvd1D/Tue/UxRwoTRLqO0/na75hX/V0WfOL7YNv0vPkM+YF1LPjEf7Pg4/+FZ+ZCOl6679QvdnmY+4F/ZMHH/4v5f/yf9O5/jf7Du7D7e+g//CYLPv5faG0z0NqIHe6nZ/vjFJ9flfofUghxAOm6dlokwOOEGur3A08DC0Zf69n+xBvRnraDKS9KGHYUHRlA21F0pB9X0UwKll2Askwn3LwFFUS6jp/yZUopLK+5M6LtCNhRUApQ6GgErTU6MoCyXHS+/ADFF25AuWRiSggH/GXjxqpep4vIJBLgp/otEMWsSh+hO/j4w3LcaOq5i2dRcsl7OPw/f8yh//ojVJ6PgmUXjHhO9xuPUbD8ojG/XttRjtzxRQ597yPkB9aRt6ACK8+Hr+Iyjt75p7j9c1F5hQwc3YOv/NJU/EhCiJGebdxY9Suni8g0EuCjxE4qu5cxeqT3H37zaPj4gS0pLyrHRfu6CTXUs/CzP2bRF+5Gh/vp3jHcHrnjhV+B5aJwzdVjfr2yXCz44++x6PN30n90DwOtjQD4K29jwR9/j5nXfpKOZ39K6ZUfoWvbo7T+diPtL/wyBT+ZEEJrbQNfdrqOTCQBPrZngKPAjNEXul7//RM6Gu5LfUm5q69xK27/XFw+P8rlxrdyPf2H3wSgO/gEobdeZtYtX0UplfB1rPwi8hevpXff6yMeH2g2be/dMxbSs/1JZr+7hnBrE+GTh6fnBxJCDFFK3dG4ser1iZ8pRpMAH0OooT4M3A2UMqq5S7T7RKiv6Y2nHSgrZ7lLZjNwZDd2uA+tNX1N2/CULaZ332t01t/PnFu/ieU55Y4HANFQB3ZfNwB2uJ++pq14yhaNeE77sz/Ff8WHwY6Ats2DykJH+qf15xIi12mtO4G/cbqOTCWrdca3C9Ni9VzMaHxI17ZHXvEuWHmBK7/4lMYvIvnyFlTgq7ico3d+GWVZeOeuoPi8Gzny48+jo2Gaf/X1oeeVveNPiHSd4MQj/8nc279FtPskx+u+a4JZ2/hWXYnvrEuGXju050W888pxF5fFXmMVR378BTxzAnjnLHfk5xUiVyilaho3VjU7XUemUrIma3y+8srZwD8BzUA4/lr+sgsDJRdUVTtSmBBCZDit9RNKqesbN1ZJCJ0hmUJPINRQ3wo8yBjbyvr2v9YYbjuyM/VVCSFEZtNadyulPiHhPTUS4BN7DGgHikdf6Nry0B+0HQmf8hVCCCHGpZT6auPGKjmueYokwCcQaqjvwyxoKxt9LdJ2pKN332uPpr4qIYTITNqOPtm4seqHTteRDSTAJ2crpk/6KYvWurc9+lqko1lOzhFCiAlobXcry/XHTteRLSTAJyHWJ/3nmNPKXKOvd7x0/4N2uL8n5YUJIURGUX/euLHqgNNVZAsJ8EkKNdQfAh4FFo6+Fu0+Eep585kHU1+VEEJkBm1Hn2j655v/1+k6sokE+OnZBBxnjA5tvQ0vNgy07H8t9SUJIUR6k6nz6SEBfhpCDfUh4IeAnzGa4HTU3/+o3d9zIuWFCSFEWlNfbtxYJac5JpkE+GkKNdS/BfwaWDT6mh7oDXdtfeQBrW079ZUJIUT60Xb0saZ/vvnHTteRjSTAz8wjwD7GWJXef2jHkf5DOzenviQhhEgvWttdynJ93Ok6spUE+BmIHXbyI8DDGOeGd77y22ejPW2HUl6YEEKkEaWsLzdurJL3wmkiAX6GQg31xzANXhYw6sQytK07X/ntAzoaGXCiNiGEcJq2o5saN1b9xOk6spkE+NQ8D7wCzB99IXziYFvvW688kvqShBDCWToa2acs10ecriPbSYBPQaih3saMwvuBotHXu4OPbQm3H92V8sKEEMIh2o72KZe7qnFjVZfTtWQ7CfApCjXUdwD/C8xmjD/Pzpfu32SH+7tTXpgQQqSY1hod7q9u3FglA5cUkABPjiDwOGNsLYv2tPV2b334Xm3b0dSXJYQQqWOH2n9w4Lu33+t0HblCAjwJYr3S7wdagZmjr/cdeONg71uvPJTywoQQIkWivZ2vuQpnfMHpOnKJBHiShBrqe4EfACWY7WUjdL/x6OsDLftfTXlhQggxzeyBvlZQNzRurJImVikkAZ5EoYb6/cB9jDGVDtD+wi8ejnaflJN4hBBZQ0cjYbu/u+rgf3zgpNO15BoJ8OR7FHiDMU4tIxqx21/45b12uK8z5VUJIcQ0iPa0ffHQf1e/4nQduUgCPMlCDfURTJe2NqBs9PVo1/Gertd+90ttRyMpL04IIZIo2tN296Hvf+yHTteRqyTAp0Goob4L+E9Mm1Xf6Ov9h988Gtr9/INa65TXJoQQyRDt7XrDVThD+pw7SAJ8moQa6g8B/wPMY4yjR3t2Pr29/9COp1JemBBCTJEd7mvDjl7fuLFKtsc6SAJ8GoUa6l8HHgAWM7pfOtD58gPPhE8cfCPlhQkhxBnSdjRi93ZvOPi9D7c4XUuukwCffr8DXmW8lenP/nRTpPtkU2pLEkKI06e1rSMdzZ869P3q55yuRUiAT7tQQ30U+AlwjDHOD9fRcLT92Z/+yu7vkS0YQoi0pbUm3NL4zcM//NSdTtciDAnwFAg11PcA/w+IAv7R1+1Qe2/HS/f/TEfCvamuTQghJmPgyO4fH7nji3/vdB1imAR4ioQa6luB72I6tRWMvh4+3nSya0vdL+QMcSFEuuk7tPPRjpfu/azTdYiRJMBTKNRQv4/hlemntFvtO/DGwa6tD/9c29FwyosTQogx9B/d83Jn/f3vi/W4EGlEAjzFQg31rwL3Ms7K9L7GLU3d2x6VRi9CCMcNtOzf3vHCL98ZaqiX7pFpSALcGQ8BTwNLx7rYu+/Vfd3Bx38lR5AKIZwSPnFob+erD94QaqiXBbZpSgLcAbHjR3+KOUd8yVjP6d1bv7dnx5P3am3L6T5CiJQKtx872Pn679/R/cZjR52uRYxPAtwhoYb6AeC/gV2Y6fRTn7PnhT09OzffLyEuhEiVSNeJlu5tj97U9drv9jldi0hMAtxBoYb6PuB7wF7GC/Fdz74Z2vXsA1oapwshplm0p72t+40/3NLx4r3bna5FTEwC3GGhhvpezMEn+xnrCFKgZ+fmHaHdz/9WQlwIMV2ifV1d3dsfv7392Z++7HQtYnIkwNNAXKOXQ4wX4juefKO34aVNkuFCiGSz+0M9PTue+ljbU3c84XQtYvIkwNNEqKG+G/h34Cgwf6zndAcf29r71su/lxAXQiRLtLezs+v133/65GM/fMDpWsTpkQBPI7FzxL8DtDJeiG979LW+/a89nNLChBBZKdrddrLjpfu/0H9k1y+crkWcPiWjufTjK68sBb4GzMAcgnKK4gtuvjQ/cP47lDqlF4wQQkwo0tna0vHivX8V7T5xR2xrq8gwEuBpyldeORMT4sXAmOfuFp59zVrfysvfpSzLldLihBAZLdx25HD78z//tu4P/W+ooV62qWYoCfA05iuvLAP+CvAxTojnB85fWnzejR9Qbk9+SosTQmSkgdamxo4XfvFtHRm4W/qbZzYJ8DTnK6+cDdQA+Zh746fwzFk+y19564ctb0FpKmsTQmSW/iO7dne8dP/fou37ZOSd+STAM4CvvHIuJsTzGGck7iqeVVh6xYc/5PL5F6S0OCFE2tNa07vv1S3dWx/+JlAn97yzgwR4hoiNxP8MmA0cHus5ylvgKb3yo7d6SudWpLQ4IUTa0rZt9+x86oXQ7ue/CTwt4Z09JMAziK+8sgj4PLAaOACc+h9PKVV6+Ydu9M5dcUmKyxNCpBkdDQ90vv77p/sPBGtDDfUvOl2PSC4J8AzjK6/0Ah8F3oYJ8TEXoRRfeMv6/KXrbpBtZkLkJnugr6ej/r7Hwi37a0MN9ducrkcknwR4BvKVV1rABuC9mOn0/jGft+rK1YWr3/ZeZbncqaxPCOGsaKj9eMcLv3oo0tH8d6GG+recrkdMDwnwDOUrr1TA5cAngeNA91jPy1+ydlHx+VUfVG6vL5X1CSGc0d/81u7Ol+5/SEf6/yXUUD9mIyiRHSTAM5yvvHIN8GWgDzg51nM8s5bO9F9624etvMKZqaxNCJE62o5GQrufe6ln5+bHgf8INdS3O12TmF4S4FnAV165BLNCPR9oHus5rsIZBf7173+P2z+nPKXFCSGmnT0Qau+sf+DZgZZ9TwI/jB1TLLKcBHiWiHVt+xLmONKD4z2v+MIN6/OXnvt2pSw5yEaILBBuP7a347mfv2r3dz8C/Ey6q+UOCfAs4iuvLAQ+DawDmoAxOy3lLTp7YfH5N90mnduEyFxaa93XtPXFrtd+txu4C3hG9njnFgnwLOMrr/QAHwKuw4zEw2M9zyoozvOvf/8tnhkLzk5lfUKIqdORgVDXloee7jvwxi7gv0IN9fudrkmkngR4FoqtUL8ReD/QBnSM99yi895xYcHyi26UrWZCZIZod9uh9hd+/lK068TzwE9CDfWdTtcknCEBnsV85ZUrgc8BRcARxurcBnjnnTWn5MINt1v5RbNSWZ8Q4vT0H92zpeOl+7ZjR+8FHg411Eedrkk4RwI8y/nKK0uAjwEXYpq+DIz1POUt8PjXv++d3llLz09heUKISdDRyEDPzqc3h/a8sBf471BD/Q6naxLOkwDPAbHObdcAH8Y0fBlzvzhA4dnXrPWVr79ZudzeVNUnhBhftKf9UOcrD7wSPnFoK/D9UEP9cadrEulBAjyH+MorA8AXgBmY0fiY//E9s5bOLLn4Pbe5fCXzU1ieECKOtiPhvsatz3ZtffgwWj8K3BdqqB9zBk3kJgnwHBPbavYRTBvWI5gObqdQLo+r5NLbr/fOXVEpB6IIkVqRruP7O1/+zbOR9qNh4H+Bl2WLmBhNAjwHxfVR/xgmwMedkstfet6SonPefouVXygL3ISYZjoa7u/d99ofut/4QwtwAvheqKH+kNN1ifQkAZ7DfOWVizCr1OcDhxin8Ytye13FF264Mm/BqiuUZblSWaMQuSLcfmx35yu/eSra2VoIvADcE2qo73G6LpG+JMBznK+8Mh+zX/w64Cgwbg9l79yzZheff9MGV2HpolTVJ0S2syMDPb17Xnyk583NHUAP8BNgm0yZi4lIgIvBKfWLgE9hRuHjH0GolCpe986L85euu05WqgsxNeETh4IdL//6OTvUUQg8jVmoNubRwEKMJgEuhvjKK+cAfwSchwnx0HjPdZfOKym+4OZ3eGYsWJOq+oTIFna4r7Nn5+aHevfW92K6Jf441FC/0+m6RGaRABcjxPaMXwxUAx4SdHADKFh+0fLC1VfdZOUXlqWoRCEyltaacGvjq50vP/Cq3d9TADwCPCjHf4ozIQEuxuQrr/QD7wOuwKyGHbffsnJ5XMXnV63PW7zmbcpye1JVoxCZJNJ1orFn59NP9R/aoYBm4P9CDfV7na5LZC4JcDGu2L3xNcDHMc1fjgDjnjXsLp1fUnxBlUyrCxEn2tvVEtrzwmO9e+vbgULgQUwf835nKxOZTgJcTMhXXlkA3AzchFml3pLo+fnLLgwUVlx2natwhqxWFznLHujt6N3/+lM9O558E60XAAcw97qbnK5NZAcJcDFpsX3jHwFWY0I84R7VgvJLy31nXXqNtGQVuURHwr19h3Y8173tkZd1ZKAM8AL3A4+HGurDDpcnsogEuDgtsUVuF2GCvBAzrZ7wSENfxRWrClZcfI2roHhOCkoUwhHajkYGju2t79r68HN2b6cP8APbgF+GGuqPOFyeyEIS4OKMxHqq3wzciGnH2jzR1xSuuersgmUXXS1tWUU20Vrr8ImD27q3PfJUpP2YBmYB+4BfAnukIYuYLhLgYkp85ZWLgQ8CZwNdJOirDoCyVOHZ15xbEDj/KivPNyMFJQoxbSIdzXu6g088PtC8txuYC7QCvwC2hhrqx2xNLESySICLKYutVl8J3AaUY7acjXvmOACWyyo65+3r8pee+zbLW+Cf/iqFSJ5o98kDPbuff7KvcctRYB6m6dF9wAtyn1ukigS4SJpYkK8GbgeWAe2xj3Epl8dVuPb6C/KXrH2b5ckrmvYihThDWmsd6Ti2u7fhpef7DgSPYg4B0phtYU+GGurH7VwoxHSQABdJF1vodg6mEcxizGi8I9HXKE++u2jt9RflLVxVaXkLSqe/SiEmR9vRSPj4gW09bz7zQvh4UxtmxO0BngDqQg317Y4WKHKWBHiWUEr9DfAhzIpwG/gM8M9Akdb6othzLgL+TWt9tVLqaszIYR+QD/xSa/2tZNYUC/LzMEE+H9PRrWuiH8RXfml5/pLzLnaVzD5LKZXMkoSYNB0Z6O0/tvflnh1PvRztPhECZmN2XtQDvwk11I9/6I8QKeB2ugAxdUqp9ZgV4RdorfuVUrMwe08B5iil3qm1fniML31Wa32zUqoQ2KqU+r3W+rVk1RVbxLPFV175BnA+5tjSAGah29gnLmmtQ3te3BPa8+IeT9niGb6Kyy/yzg6cr9zegmTVJUQi0d6u5v5DO17uefOZN3S4L4oJ7jnALuDeUEP9PmcrFMKQAM8O84HjWut+AK31cYDY6PVfga8DYwU4sef3KKVeA1YASQvwQaGG+ijwqq+8civmoJTbMUHeQoITz8InDrZ1vPDLx5Qn7ylfxZVn5y9ec7HLV7ow2fUJobVtR9qP7erd99rLfY1bmgAXJrS9QBCoQ7aEiTQjU+hZQClVBDwH+IDHgV9prTcrpZ4Gvgr8C/BtzPR1/BT6V2Mj8DJMcFdprXdMd72+8kovUIlZtV4SqyvxqvWYvEVrFhSsuORiz8yF5yjLJf8AFVOiIwOh/ua3XgvtevbVSPuxTkxgzwUU5v9Tfwg11B9ytEghxiEBniWUUi7gSuAazP3vGuBjmAAvAf4G+Bpj3wO3gf/VWv8glTXHgvwcTI/1FZiDUppJcGDKIMvnzy9c/bbz8+avvMjKK5w5vZWKbKLtaCTS0dLQf3T39t6Gl3bryEAUc2+7DBgA/gBsDjXUn3C0UCEmIAGehZRSt2HO8y7GjLJfVUo9j2kwcdvoEbhzlRqx7WcLgatiHx7MiDzxgreYghWXrMgPnHeRu2TOWTIqF2PR2rajXSf29x9tCPburd9l93X1Y0bZZZjwbsNMk78UaqhP2ONfiHQhAZ4FlFIVgK21boh9/vdAKWZ0OxjgNwE/APalW4DHi7VovRAzKp8L9GPulU/Y1UrlFXp9Ky4q984rX+0umVOuXG7vRF8jspfWGrun7UB/81vbe/e+vCO2khzMPxDnYNYA7QAeBXbG1moIkTEkwLOAUupC4HuY0I4Ae4FPY05A+qrW+tXY814DutI5wAfFtqCdBVyLWfgGZvV672S+Xnny3AXLL1rhnb9ytad0XoVyefKnqVSRZqKhzqMDLfu29771yvZI+9HOuEslmHPtw8CTwDNyyIjIZBLgIu35yitnYha9vRNzW6AbM8U+ub+8LrdVsOyCZXkLVq12z1iwynJ7C6etWOEIu6/nxMDxxmDvvte3h1v3x9+7LgRmYqbLD2MWeb4q0+QiG0iAi4zhK6/0AGuAd2BatmpMq9bOBF82klIqf+m6JXmL1qz2zFy42vLkl0xHrWJ6aW3bdqjjSLj92L7+A8E3+4/sim+qko85EczCzNo8BWwFjsk2MJFNJMBFRvKVV87DnID2Nky7VjBB3sFkR+ZA3uJzFuYvOnu1e8b8s6z84rnS+S09aa2x+7qORdqb9w+07t/fdyDYpPt7BuKe4sU0XHFh/h48DbwOHJTQFtlKAlxkPF955SzMgr3LMdvRFGYFexunEeaWr7Qgf/E5Sz2zlgTc/jkBCXRn2X09JyKdLfsHWhv39R/c3hjtaRu9/sGNCW0PpiHQc8ArwH45ylPkAglwkVV85ZUzMNPrVwCrYg+HMPfMT+tNPS7Ql7pLZi22CkrmK2VZya1YDLIHejsina37wycO7u8/uGN/pOPYWNsIXZjp8TzMYrQXMb3JG0IN9RP2DxAim0iAi6zlK68sxoT5euBczD3RXsyhKqe9ZUh58tx5C1bN98xautjtn7vIVTRjkeXJL05q0TlCR8P9dm93SzTU3hzpaDnaf/jN/eETB9rGeKqFWT0++OccBbYAzwO7Qw31/SkqWYi0IwEucoKvvNKHGZFXAhdgggHM/dIuTnN0Psg9Y4HfO++she7iWbNcvtJZVkFxmZVXOEv2oBvatqN2f/dxO9TZHOk+0RLpaG4JtzY1j9reFU8BRYA/9nuAtzCtfvcCB0IN9eHpr1yI9CcBLnKOr7wyH1iC2Wd+PrA87vKUAn2Qq2ROkads0Sx3yZwyV+GMMpfPP8vKLypT3oIZKgtvrGut0QO9bdHezuZoT1tLtKOlJXziYPPA8aaT2NGJ/iwLMT0MXLHPD2ACew/QFGqo75u+yoXIXBLgIuclCHSFuX/eiemRPWXK5XF5yhbPcM9YMMtVXFbmKiwts/KLZyq316dcngLlcuenYztYHY0M6HB/lx3u69QDoS67P9Rp93V1RUOdndGek50DLY2teiA02ZFxASaw3Zg/42ZMYO8CGkMN9WMfNSuEGEECXIhRYoG+ELM97RzM1LsvdjmKCfQQZ3AffTKUJ9/t8pUWWAXFBVZ+UYGVV1hg5fkKlCe/wPLmFyh3XoHy5BUot9d8WO480BqNrdEarTVoe8Sv5sKox8zvtW1HdWSgV4f7QvZAb8juD4Xs/p6Q3dcVskOdoWjPyR67r/tM/gHjxoyuizCjaxtz6+IkZl/2DsyK8fbk/MkJkVskwIWYQOywlTJMoK/ANJNZiAkojQklGxPqvUAfp7F9LQtYmH/gFGFWh0djj/UDTZgT75owPe1bpAuaEMkhAS7EGYiFuh8T7LOA+Zhp+MWxx3Tsw4UJssFwz9QFWArTLMWL6XRWwPA/UjSmTem+2EcLZlq8Q5qoCDF9JMCFSLJYy9cyhsN9MNgXYcIvflHX4Gr4KCbcI3G/Dn5M5/9JPeN8EFenin10YLbgtWKC+hgmqE/ISV5CpJ4EuBApEhu1Dx6uURD34cPsdY7/KMZMSQ/eex8c0YMJ06n8H1fFffRg7ukPtqFtx9yj7mB4RX4n0C0hLUR6kQAXIo3FjlXNY2Tg58Uu25ggH/xVT/KxKGY6v1dajgqRuSTAhRBCiAwkfZ2FEEKIDCQBLoQQQmQgCXAhhBAiA0mACyGEEBlIAlwIIYTIQBLgQgghRAaSABdCCCEykAS4EEIIkYEkwIUQQogMJAEuhBBCZCAJcCGEECIDSYALIYQQGUgCXAghhMhAEuBCCCFEBpIAF0IIITKQBLgQQgiRgSTAhRBCiAwkAS6EEEJkIAlwIYQQIgNJgAshhBAZSAJcCCGEyEAS4EIIIUQGkgAXQgghMpAEuBBCCJGB/j9t/be25HVkngAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = np.asarray(impact_data['Variant_Type'].unique())\n",
    "sizes = mut_types\n",
    "explode = (0.1, 0.1, 0.1, 0.1,0.1,0.1,0.1)  # only \"explode\" the 2nd slice (i.e. 'Hogs')\n",
    "\n",
    "fig1, ax1 = plt.subplots(figsize=(7,7))\n",
    "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "#plt.savefig('Pie_mutation_rep.png', dpi=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## assumptions\n",
    "\n",
    "- Assuming that RT template length is not limiting for engineering deletions\n",
    "- However, RT length must be ≥ size of insertion in order for it to work\n",
    "    - This doesn't account for the need for homology arms. RT template length can be thought of here as the template length with edits included, but excluding the extra basepairs needed for homology...\n",
    "    - I'm IGNORING the necessity for a region of homology in the RT template. This simplifies the analysis, and any homology requirements can be very simply added to the this analysis without changing the code (e.g. if 10 bp of homology required, and edit can be captured with RT template length of 7 as calculated here, then total RT template length size = 17)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def coverage_classifier(impact_data, df1, rt_template_length):\n",
    "    #load in (1) impact_data, \n",
    "    #(2) df containing gene coordinate info\n",
    "    #(3) length of rt template\n",
    "    \n",
    "    filepath = '/Users/samgould/Desktop/FSR Lab/2022-06-21/'\n",
    "\n",
    "    PE_domain_boundary = np.load(filepath + 'PE_coverage_boundary_NGG_plusminus_1000bp.npy', allow_pickle=True)  \n",
    "    \n",
    "    filepath_coverage = '/Volumes/Sam_G_SSD/PE coverage NGG npy arrays/' + 'PE_coverage_NGG_rt' + str(rt_template_length) +'.npy'\n",
    "    PE_domain_coverage = np.load(filepath_coverage, allow_pickle=True)\n",
    "    #for plus and minus strand\n",
    "    \n",
    "    path = '/Volumes/Sam_G_SSD/human genome GrCh37 IMPACT genes/'\n",
    "    unique_genes = np.load(path + 'human_impact_genes_NAMES.npy', allow_pickle=True)\n",
    "    unique_genes = list(unique_genes)\n",
    "    \n",
    "    #iterate through mutations in IMPACT dataset\n",
    "    num_mutations = len(impact_data)\n",
    "    \n",
    "    #initialize array of zeros for holding information about coverage\n",
    "    zeros = np.zeros(num_mutations)\n",
    "    \n",
    "    #initialize array for holding weird cases with bad location\n",
    "    outside_domain = []\n",
    "    \n",
    "    for i in range(num_mutations):\n",
    "\n",
    "        #get info on current mut\n",
    "        mut = impact_data.iloc[i]\n",
    "        \n",
    "        #getting type of mutations\n",
    "        mut_type = mut['Variant_Type'] \n",
    "        \n",
    "        #getting gene and start/end position of mutation\n",
    "        gene = mut['Hugo_Symbol']\n",
    "        gene_index = unique_genes.index(gene) #retrieving index\n",
    "        \n",
    "        seq_start = df1[df1['gene']==gene]['gene_start'].values[0] \n",
    "        \n",
    "        s = mut['Start_Position']\n",
    "        e = mut['End_Position']\n",
    "        size_mut = (e-s)+1 #size of mutation\n",
    "        \n",
    "        #re-indexing with start of gene as position 0\n",
    "        start_true = s-(seq_start-1001)\n",
    "        end_true = e-(seq_start-1001)+1\n",
    "    \n",
    "        #retrieving the appropriate gene's prime editing domain\n",
    "        PE_domain_bound = PE_domain_boundary[gene_index]\n",
    "        PE_domain = PE_domain_coverage[gene_index]\n",
    "    \n",
    "        #first checking for weird boundary misc.\n",
    "        if (start_true or end_true)>len(PE_domain):\n",
    "            outside_domain.append(i)\n",
    "            \n",
    "        else:\n",
    "\n",
    "            if mut_type == 'SNP':\n",
    "                val = PE_domain[start_true]\n",
    "\n",
    "                if val >0:\n",
    "                    zeros[i] +=1\n",
    "                else:\n",
    "                    continue\n",
    "\n",
    "            elif mut_type =='DEL':\n",
    "                #only need to check that the start is in PE domain (not accounting for homology arms)\n",
    "                val = PE_domain[start_true]\n",
    "\n",
    "                if val >0:\n",
    "                    zeros[i] +=1\n",
    "                else:\n",
    "                    continue\n",
    "\n",
    "            elif mut_type == 'INS':\n",
    "                #first check rt template length is at least size of insertion\n",
    "                if rt_template_length >= size_mut:\n",
    "                    pe_cover = PE_domain[start_true:end_true]\n",
    "\n",
    "                    if all(kk>0 for kk in pe_cover):#then check coverage)\n",
    "\n",
    "                        plus_end = PE_domain_bound[0] \n",
    "                        minus_end = PE_domain_bound[1]\n",
    "                        #finally check boundary condition\n",
    "                        diff_sizes = rt_template_length-size_mut\n",
    "\n",
    "                        bounds_plus = plus_end[start_true-diff_sizes:start_true+1] \n",
    "                        bounds_minus = minus_end[end_true:(end_true+diff_sizes+1)]\n",
    "\n",
    "                        #if start site is anywhere within these bounds, boundary condition satisfied\n",
    "                        sum_bound = np.sum(bounds_plus)+np.sum(bounds_minus)\n",
    "\n",
    "                        if sum_bound>=1:\n",
    "\n",
    "                            zeros[i] +=1\n",
    "                        else:\n",
    "                            continue\n",
    "\n",
    "                    else:\n",
    "                        continue\n",
    "\n",
    "                else:\n",
    "                    continue\n",
    "\n",
    "\n",
    "            elif mut_type == 'DNP':\n",
    "                if rt_template_length >= 2:\n",
    "                    pe_cover = PE_domain[start_true:end_true]\n",
    "\n",
    "                    if all(kk>0 for kk in pe_cover):#then check coverage)\n",
    "\n",
    "                        plus_end = PE_domain_bound[0] \n",
    "                        minus_end = PE_domain_bound[1]\n",
    "                        #finally check boundary condition\n",
    "                        diff_sizes = rt_template_length-size_mut\n",
    "\n",
    "                        bounds_plus = plus_end[start_true-diff_sizes:start_true+1] \n",
    "                        bounds_minus = minus_end[end_true:(end_true+diff_sizes+1)]\n",
    "\n",
    "                        #if start site is anywhere within these bounds, boundary condition satisfied\n",
    "                        sum_bound = np.sum(bounds_plus)+np.sum(bounds_minus)\n",
    "\n",
    "                        if sum_bound>=1:\n",
    "\n",
    "                            zeros[i] +=1\n",
    "                        else:\n",
    "                            continue\n",
    "\n",
    "                    else:\n",
    "                        continue\n",
    "\n",
    "                else:\n",
    "                    continue\n",
    "\n",
    "\n",
    "            elif mut_type == 'ONP':\n",
    "                if rt_template_length >= size_mut:\n",
    "                    pe_cover = PE_domain[start_true:end_true]\n",
    "\n",
    "                    if all(kk>0 for kk in pe_cover):#then check coverage)\n",
    "\n",
    "                        plus_end = PE_domain_bound[0] \n",
    "                        minus_end = PE_domain_bound[1]\n",
    "                        #finally check boundary condition\n",
    "                        diff_sizes = rt_template_length-size_mut\n",
    "\n",
    "                        bounds_plus = plus_end[start_true-diff_sizes:start_true+1] \n",
    "                        bounds_minus = minus_end[end_true:(end_true+diff_sizes+1)]\n",
    "\n",
    "                        #if start site is anywhere within these bounds, boundary condition satisfied\n",
    "                        sum_bound = np.sum(bounds_plus)+np.sum(bounds_minus)\n",
    "\n",
    "                        if sum_bound>=1:\n",
    "\n",
    "                            zeros[i] +=1\n",
    "                        else:\n",
    "                            continue\n",
    "\n",
    "                    else:\n",
    "                        continue\n",
    "\n",
    "                else:\n",
    "                    continue\n",
    "\n",
    "\n",
    "            elif mut_type == 'TNP':\n",
    "                if rt_template_length >= 3:\n",
    "                    pe_cover = PE_domain[start_true:end_true]\n",
    "\n",
    "                    if all(kk>0 for kk in pe_cover):#then check coverage)\n",
    "\n",
    "                        plus_end = PE_domain_bound[0] \n",
    "                        minus_end = PE_domain_bound[1]\n",
    "                        #finally check boundary condition\n",
    "                        diff_sizes = rt_template_length-size_mut\n",
    "\n",
    "                        bounds_plus = plus_end[start_true-diff_sizes:start_true+1] \n",
    "                        bounds_minus = minus_end[end_true:(end_true+diff_sizes+1)]\n",
    "\n",
    "                        #if start site is anywhere within these bounds, boundary condition satisfied\n",
    "                        sum_bound = np.sum(bounds_plus)+np.sum(bounds_minus)\n",
    "\n",
    "                        if sum_bound>=1:\n",
    "\n",
    "                            zeros[i] +=1\n",
    "                        else:\n",
    "                            continue\n",
    "\n",
    "                    else:\n",
    "                        continue\n",
    "\n",
    "                else:\n",
    "                    continue\n",
    "\n",
    "\n",
    "            elif mut_type == 'UNK':\n",
    "                #IGNORING UNCLASSIFIED MUTANTS\n",
    "                continue\n",
    "\n",
    "    return zeros, outside_domain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "complete rt template length = 1\n",
      "complete rt template length = 2\n",
      "complete rt template length = 3\n",
      "complete rt template length = 4\n",
      "complete rt template length = 5\n",
      "complete rt template length = 6\n",
      "complete rt template length = 7\n",
      "complete rt template length = 8\n",
      "complete rt template length = 9\n",
      "complete rt template length = 10\n",
      "complete rt template length = 11\n",
      "complete rt template length = 12\n",
      "complete rt template length = 13\n",
      "complete rt template length = 14\n",
      "complete rt template length = 15\n",
      "complete rt template length = 16\n",
      "complete rt template length = 17\n",
      "complete rt template length = 18\n",
      "complete rt template length = 19\n",
      "complete rt template length = 20\n",
      "complete rt template length = 21\n",
      "complete rt template length = 22\n",
      "complete rt template length = 23\n",
      "complete rt template length = 24\n",
      "complete rt template length = 25\n",
      "complete rt template length = 26\n",
      "complete rt template length = 27\n",
      "complete rt template length = 28\n",
      "complete rt template length = 29\n",
      "complete rt template length = 30\n",
      "complete rt template length = 31\n",
      "complete rt template length = 32\n",
      "complete rt template length = 33\n",
      "complete rt template length = 34\n",
      "complete rt template length = 35\n",
      "complete rt template length = 36\n",
      "complete rt template length = 37\n",
      "complete rt template length = 38\n",
      "complete rt template length = 39\n",
      "complete rt template length = 40\n",
      "complete rt template length = 41\n",
      "complete rt template length = 42\n",
      "complete rt template length = 43\n",
      "complete rt template length = 44\n",
      "complete rt template length = 45\n",
      "complete rt template length = 46\n",
      "complete rt template length = 47\n",
      "complete rt template length = 48\n",
      "complete rt template length = 49\n",
      "complete rt template length = 50\n"
     ]
    }
   ],
   "source": [
    "rt_template_length = np.linspace(1,50,50) #iterating through all RT template lengths from 1 to 50\n",
    "\n",
    "one_to_fifty = []\n",
    "outside = []\n",
    "for x in rt_template_length:\n",
    "    \n",
    "    rt_template_length = int(x)\n",
    "       \n",
    "    coverage, outside_domain = coverage_classifier(impact_data, df1, rt_template_length)\n",
    "    \n",
    "    one_to_fifty.append(coverage)\n",
    "    outside.append(outside_domain)\n",
    "    \n",
    "    print('complete rt template length = ' + str(rt_template_length))\n",
    "\n",
    "    \n",
    "filepath = '/Volumes/Sam_G_SSD/PE coverage quant NGG/'\n",
    "np.save(filepath + 'MSK_PE_coverage_NGG_rt_1to50' + str(rt_template_length) +'.npy', np.asarray(one_to_fifty))\n",
    "np.save(filepath + 'outsidedomain_rt1to50' + str(rt_template_length) +'.npy', np.asarray(outside))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-20.,   0.,  20.,  40.,  60.,  80., 100., 120.]),\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "perc_coverage = [(sum(i)-1)/(len(i)-1)*100 for i in one_to_fifty]\n",
    "rt_template_length = np.linspace(1,50,50) #iterating through all RT template lengths from 1 to 50\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(rt_template_length,perc_coverage)\n",
    "plt.scatter(rt_template_length,perc_coverage)\n",
    "plt.ylim(-5,105)\n",
    "plt.ylabel('Percent Coverage of MSK IMPACT mutations', fontsize=15)\n",
    "plt.xlabel('RT Template Length (excl. homology)', fontsize=15)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "#plt.savefig('total_mutations.png', dpi=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating PE Coverage Figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath = '/Volumes/Sam_G_SSD/PE coverage quant NGG/MSK_PE_coverage_NGG_rt_1to50.npy'\n",
    "cover_list = np.load(filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "perc_coverage = [(sum(i)-1)/(len(i)-1)*100 for i in cover_list]\n",
    "#there's a single mutation that doesn't behave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-20.,   0.,  20.,  40.,  60.,  80., 100., 120.]),\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "rt_template_length = np.linspace(1,50,50) #iterating through all RT template lengths from 1 to 50\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(rt_template_length,perc_coverage)\n",
    "plt.scatter(rt_template_length,perc_coverage)\n",
    "plt.ylim(-5,105)\n",
    "plt.ylabel('Percent Coverage of MSK IMPACT mutations', fontsize=15)\n",
    "plt.xlabel('RT Template Length (excl. homology)', fontsize=15)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "#plt.savefig('total_mutations.png', dpi=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For further analysis, break down into subcategories of mutations (SNPS, INS, DELs, etc.)\n",
    "\n",
    "## ALSO, need to exclude the indeces that fall outside of the domain. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ONP = np.asarray(impact_data[impact_data['Variant_Type']=='ONP'].index)\n",
    "SNP = np.asarray(impact_data[impact_data['Variant_Type']=='SNP'].index)\n",
    "DNP = np.asarray(impact_data[impact_data['Variant_Type']=='DNP'].index)\n",
    "TNP = np.asarray(impact_data[impact_data['Variant_Type']=='TNP'].index)\n",
    "INS = np.asarray(impact_data[impact_data['Variant_Type']=='INS'].index)\n",
    "DEL = np.asarray(impact_data[impact_data['Variant_Type']=='DEL'].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#not the prettiest, or most efficient way to do this, but it works\n",
    "#could be re-written in about 1000 different ways to be faster\n",
    "ONP_cover = []\n",
    "SNP_cover = []\n",
    "DNP_cover = []\n",
    "TNP_cover = []\n",
    "INS_cover = []\n",
    "DEL_cover = []\n",
    "\n",
    "ONP1 = []\n",
    "SNP1 = []\n",
    "DNP1 = []\n",
    "TNP1 = []\n",
    "INS1 = []\n",
    "DEL1 = []\n",
    "\n",
    "for i in cover_list: #iterating through different rt template lengths\n",
    "    sumONP = 0\n",
    "    sumSNP = 0\n",
    "    sumDNP = 0\n",
    "    sumTNP = 0\n",
    "    sumINS = 0\n",
    "    sumDEL = 0\n",
    "    \n",
    "    for k in ONP:\n",
    "        sumONP+=i[k]\n",
    "    \n",
    "    for k in SNP:\n",
    "        sumSNP += i[k]\n",
    "        \n",
    "    for k in DNP:\n",
    "        sumDNP += i[k]\n",
    "    \n",
    "    for k in TNP:\n",
    "        sumTNP += i[k]\n",
    "        \n",
    "    for k in INS:\n",
    "        sumINS += i[k]\n",
    "        \n",
    "    for k in DEL:\n",
    "        sumDEL += i[k]\n",
    "        \n",
    "        \n",
    "    #del = counts[0]\n",
    "    #dnp = counts[1]\n",
    "    #ins = counts[2]\n",
    "    #onp = counts[3]\n",
    "    #snp = counts[4]    \n",
    "    ONP_cover.append(sumONP/(len(ONP)))\n",
    "    SNP_cover.append(sumSNP/(len(SNP)))\n",
    "    DNP_cover.append(sumDNP/(len(DNP)))\n",
    "    TNP_cover.append(sumTNP/(len(TNP)))\n",
    "    INS_cover.append(sumINS/(len(INS)))\n",
    "    DEL_cover.append(sumDEL/(len(DEL)))\n",
    "    \n",
    "        #onp = counts[3]\n",
    "    #snp = counts[4]    \n",
    "    ONP1.append(sumONP)\n",
    "    SNP1.append(sumSNP)\n",
    "    DNP1.append(sumDNP)\n",
    "    TNP1.append(sumTNP)\n",
    "    INS1.append(sumINS)\n",
    "    DEL1.append(sumDEL)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "c_list = [plt.cm.Blues(0.6), 'tab:orange',plt.cm.Purples(0.6),'tab:pink', 'tab:cyan']\n",
    "\n",
    "plt.plot(rt_template_length,np.asarray(SNP_cover)*100, label='SNP', color=plt.cm.Blues(0.6), linewidth=4, alpha=0.5)\n",
    "#plt.plot(rt_template_length,TNP_cover, label='TNP' )\n",
    "plt.plot(rt_template_length,np.asarray(DEL_cover)*100, label='DEL', color=plt.cm.Purples(0.6),linewidth=4, alpha=0.5)\n",
    "\n",
    "\n",
    "plt.plot(rt_template_length,np.asarray(INS_cover)*100, label='INS' ,color='tab:cyan', linewidth=4, alpha=0.5)\n",
    "plt.plot(rt_template_length,np.asarray(DNP_cover)*100, label='DNP', color='tab:pink', linewidth=4, alpha=0.5)\n",
    "\n",
    "\n",
    "plt.plot(rt_template_length,np.asarray(ONP_cover)*100, label='ONP' , color='tab:orange', linewidth=4, alpha=0.5)\n",
    "plt.plot([0,50], [100,100], linestyle='dashed', c='tab:grey', linewidth=2)\n",
    "\n",
    "\n",
    "plt.scatter(rt_template_length,np.asarray(ONP_cover)*100, color='tab:orange')\n",
    "plt.scatter(rt_template_length,np.asarray(SNP_cover)*100, color=plt.cm.Blues(0.6))\n",
    "plt.scatter(rt_template_length,np.asarray(DNP_cover)*100, color='tab:pink')\n",
    "#plt.plot(rt_template_length,TNP_cover, label='TNP' )\n",
    "plt.scatter(rt_template_length,np.asarray(INS_cover)*100, color='tab:cyan')\n",
    "plt.scatter(rt_template_length,np.asarray(DEL_cover)*100, color=plt.cm.Purples(0.6))\n",
    "\n",
    "plt.legend(fontsize=16)\n",
    "\n",
    "plt.ylim(-5,105)\n",
    "plt.xlim(0,40)\n",
    "plt.ylabel('Percent Coverage (≥1 pegRNA/mutation)', fontsize=15)\n",
    "plt.xlabel('RT Template Length', fontsize=16)\n",
    "plt.xticks(range(0,41, 5),fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "#plt.grid(axis = 'x', linestyle='dashed')\n",
    "\n",
    "\n",
    "plt.savefig('RT_template_length_coverage_NGG.png', dpi=250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SNP    348145\n",
       "DEL     49838\n",
       "INS     17784\n",
       "DNP      6042\n",
       "ONP      1011\n",
       "UNK         1\n",
       "TNP         1\n",
       "Name: Variant_Type, dtype: int64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_size = [mut_types[0], mut_types[1], mut_types[2], sum(mut_types[3:])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vDNUBiIgGEAEgAKAEQBGAbAA5Pf+yAWRbtgzELTvHkjLXtpEtIQMAXFJCg4AGQEBCQECDhBACmuz5CsCWQEQAQSFESBMI6kJ0G5roMnQtCCDc8y8EoB1AC4Dmnq8tAOIpvTeIiIhlmYgcwQAwFEAlgFIAJaZlD46YdoUlZZkGUaJrotBtaLmQUnTHrEhX1LRCMQuhmCW6o6YWjFlGd9RyheOWHjVt7PzPsiWklJBA4p+UkHLHfwO2lAAAIQC3rsFtaPD0fHXrGjyGgMfQpcfQTK9Ls7wu3crxGFauz5A5XkMLuA3Da2geS8pY3JJdli3bbchmDaLRbWi1HkPbBGALgK09XxsB2Km/m4mIMo+QPU/iRERpTEOiBFcCGG5LOSIUs8bbUo4xNG2ox9DygzEz0tQdMxu7YlpLMOZpCcXc7WETHeE42j//ZyJqDtyOGXDryPYayPEYyPEayPYayPe5MCjHEy3N8UYHZbtlvt/t8RiaETXtFtO2twkhqvwufa2uiRoAGwGsQaJM88mfiGgvsCwTUTrRAYwEMNGWclIwas0UAgd4XXpZJG7Fmrtj8W0dEW1bR8Rf1xnVG7qiaOiKork7BtN2znOdx9BQGHCjJMuN4iw3SrI8sjTXG6oo8JlDcjxeXQg7atqbhcDyLI+xGMBaJEp0NQBTbXoiooGFZZmIBiIBoBzAVCnlpO6YNVMAk70ubWh31IpuaQ1bG5qDgeqWkLGlLYza9siAHhEeaHK8BobmeVGe58OwAl98RKE/VJ7n07O9hjcSt+qkxPIsj/6BEGIRgMVIzJ8mInIklmUiGgjyARxo2fLgYMw62q2L6ZaU7s0t4diGpqC/ujXkqmkNY2tbGBGW4n7j1gXKcr2oLPRjbElWbMLgrPDQfJ/ftGSrZctFuxToNtV5iYhSgWWZiFLNA2AqgJldUXOOJjDLpWtFW1rDoRV1nf61Dd2udY1BNHXHFMckANAEUJbrxajiAMaUBGITB2eHh+b5fKYt2y1bLsj2Gq8D+BDASvCgQiLKQCzLRNTfPABmmpZ9TDhuf8Xr0iY1dcciq+q6XKvru3zrmoLY2haGg6YUpz0BoCzPizHFAUwpyw1PLc8xczyGHjHtBdke/RUhxEdIjD5zqTsiSnssy0SUbC4AB1m2PDoYs07zubQp9V3RyMIt7b6ltZ2uVfVdCMc5AJlp8n0uTByShSmlOdGp5bnR4iy3Jxy3lwXc+mu6Jt4H8BmAqOKYRET7jGWZiPaXQGJ1ilO6o9bpPpc2vak7Fl2wpd27bFune2VdF4IxS3VGSrEst47xg7MxuTQ7fmBFXnhIjscTidvzsr3GswDeALABXL6OiNIAyzIR9YUXwFHhuHUOgDNipu3/tLpNX7S1w7OirhPdUZZj+qJsj4Gp5Tk4eFhe6MChedA10S0EXvK59OcBvIfEzoVERAMOyzIR7a0hAE7uiphf9bi0w7a2hWMfbWrNml/TrtW0sefQvhlW4MNBFXn2ESMKuocV+HaMOv8TwIsAtqvOR0S0A8syEX2ZCXHLviBi2ue7NDFscW1H/JOqtqxFWzvQFeXeFZQcWW4d04bm4rDh+aGDKvL0uCXXBdz6k5omnkNiC28iImVYloloV6NMy/5q1LSvsCRK3t/YbHxS1eZe09ANi0tWUD8zNIGpZTmYPbowPKsyX5i2rAq49T9pQjwLoEZ1PiJyHpZlIgKAYZYtzw/Hra8LoPzDTa3ivQ3N3tX13TwCi5QxNIHJpTk4alRB+NDhBcKWssbn0p/UNfEMgCrV+YjIGViWiZyr1LblecG49XVDEyM/rmqV765v9q2s6+KaxzTg6JrAAUOycdSowsjhIwpgS7khy2M8AuBfADpU5yOizMWyTOQsHgCnd0fNbxmaOPCzmnb73fXN/qXbOjnFgtKGrglML8/FyRNKuqeW5Rhxy34j4DF+C+AdAFyKhYiSimWZyBmmh2PWNzVNXFjVHLRfWdWYPXdzG6ImNweh9JbtMXDUqAJ56sRB3cVZblMI8UePof0RwFrV2YgoM7AsE2WuXFvKC8Mx69txW5a+tqrR/da6JqOxO6Y6F1G/GJbvw3HjimPHjS2yAFRleYyHAfwNQLfiaESUxliWiTLPwaGYdZOhiTOWbuuwXl7ZGFhS28ED9cgxNAHMGJqLUyYOCk4pzdEsKZ/yufTfAFinOhsRpR+WZaLM4AZwTjBq3haz7GEvLG/wvb2+SWsPcy1kcrbiLDdOmVASP3liiSkllmR5jPsAvArObSaivcSyTJTeiuOWfa1lyxs3t4aNZ5Zsz56/pZ2rWRDtwqULHD6iAOdMHdI1KNsTcenaA4Ymfg+gRXU2IhrYWJaJ0tPkUMz6nq6Jsz6papXPLavzbW7lltNEe2N0cQBnTB4cOqQyX7Ns+YLfrd8HYKnqXEQ0MLEsE6UPHcBXuqPmbVJi4gsr6t2vrW7UOyOcakHUFzleAyeOL7bOmjwkJgQWZ3mMHwH4AOAUfyL6fyzLRAOfC8CFoZh1T1N3NO9fi7dnfVLdBpNzLYiSwtAEjh5TKC+aUR7yubTqQKI0vwyAaysSEcsy0QDmsWx5Rcyy76xpDfufXlCbtWxbp+pMRBlLE8Ahlfn42kHl3UUBd5vfrd8O4O8A4qqzEZE6LMtEA0/AsuU34pZ9+/rGoOvpBbVZaxq4TCxRKk0ty8FFB5Z1Dy/0R9y6do+uiT8ACKrORUSpx7JMNHDkxC37BlvKW5dt69L+trA2sLE5pDoTkaONLg7gwhmlwSllObYmxE9duvYwAP5hEjkIyzKRejlR0/4ugJsXbGkXf1u4zb+ljStbEA0kQ/O8uPzgoaEpZTkxl67drmvidwC4HSaRA7AsE6njMS37WtOWd87f0u56en6tr64zqjoTEX2JkUV+XDmrontMSSDsMbTvaUL8BQCXpCHKYCzLRKmn21JeFDXtX65t6Pb/Ye6WANdIJkov4wdl4apDK7qH5vna/W79FgDPgKtnEGUklmWi1BEATgnFrIfqOiPFT3xSk7WqngfuEaWzqWU5uOrQiu7iLE9DwK3fDOAVcJ1moozCskyUGocFY+YjXRFz9O8+3RKYV9OuOg8RJdGsYXn4+qEVwRyva0PArV8FYKHqTESUHCzLRP1rbDBmPhq35CFPfrbV9+6GZsG9RIgykyaA48YWyytmDY1oQrzod+vfBlCnOhcR7R+WZaL+kROJWz8BcPXfF21zv7SyQY9b/FsjcgKfS8OFM8pip0wcZArgXreh3Q8gojoXEfUNyzJRcmm2lBfFTPvBzza3e//w2RZfW4ibfxE50eAcD7552LDgpCHZQZ9Lvw7Ac+B8ZqK0w7JMlDwzQjHrT03d0REPflCdta6Rm30RETC5NBvXHzk8mOcz1gbcxlUAlqjORER7j2WZaP8Vh2LWr2wpz/njZ1u9b61tEvyrIqKdaQI4flyxvOLgoREhxDN+t34TgDbVuYhoz1iWifrOMC37OtOW97y1rtn1lwW1nmDMUp2JiAYwv1vHFQcPjcwZXRjxuvRrAfwTnJpBNKCxLBP1zeRQzPrnlrZwxW/erwpsbeexO0S098YNysJ35owI5vqMxQG3cSmAatWZiGj3WJaJ9o03alp3WTau//3cLd431zYJ1YGIKD3pmsBZkwebF0wvjWtC3NOzagaPCCYaYFiWifbeEeGY9ffldZ0FD3+42c9VLogoGQZne3DT7OHBkUWBBr9bvwjAZ6ozEdH/Y1km2rPccMz6tWnLCx78oNo3dzOPySGi5DtyZIG87ojKiK6Jf/hc+rcBdKjOREQsy0R7clokbj35UVWr//efbvHyAD4i6k9Zbh1fP7QicsSIgqDXpV8I4E3VmYicjmWZaPcKQzHrT6G4deyv3t3kX769S3UeInKQaeU5+O7RI0NuQ/u3z6XfAIBPQkSKsCwT/a/jI3HrH2+ubQo8OW+rJ8ZtqolIgYBbxzWHDwvPqszv9Ln0CwC8rzoTkROxLBP9P184bj0Qt+xL7n17k3/Ztk7VeYiIcFBFHr4zZ0TIpYs/el36rQC4ViVRCrEsEyVMC8es/yzd1lnymw+q/N1Rzk0mooEjx2vg5tkjQpOGZDf43fqZAJapzkTkFCzL5HR6zLS/Z0n5o0c/2ux9b0ML100mogHr6DGF8trDKyO6EHe7De0+ALbqTESZjmWZnGxYKGY9u7U9PP7nb20MNHXHVOchItqj4iw3fnjcqGB5nm+p362fDaBBdSaiTMayTI5kS3lBzLR//4/F273/WVZn2PwzIKI0ogngaweWx08/YFC316WfDeA91ZmIMhXLMjmNJxy3Hg3GrK/e9fp6/6bmkOo8RER9NrUsBz84blTYpYsHPIb+YwA84IIoyViWyUkqQzHrtZV1XcPuf3eTP8QNRogoA+T7XbjtuFHBYQX+FT0H/9WrzkSUSViWySlOjcStv/914Tb/88vrddVhiIiSSRPAhTPK4mdOHhz0uvRzAbytOhNRpmBZpkxnROLWvVHLvvbu1zf41jR0q85DRNRvJpdm44fHjQ67dPGQ16X/CICpOhNRumNZpkw2JBSzXqpqCU746Zsb/Z0RvmYQUebL8xn44XGjg8ML/Sv9bv1UAM2qMxGlM5ZlylSzI3Hr+f8srw/8Y9E2F1e7ICIn0QRw+cFDYydNKGnzufTjASxXnYkoXbEsU6YRccu+MWbZP/v5Wxt9S2q5ZTUROddRIwvkDUcND7sN7RJNiOdU5yFKRyzLlEk8oZj1x/Zw/MwfvbrW39DFTUaIiEYV+fGTk8eGvIb2kNel3wbu+ke0T1iWKVMMCsWs11fVd4257+2N/nCcrwVERDvkeg385OSxwbJc79yeXf/4sRvRXmJZpkwwJRK33n5hRX3uXxdsc/ERTfvLpQsMyfZgSK4XxVluFAbcyPEYCHgMBNw6/G5huw1hu3QJQwN0TQhdA4QQQghAABAQkJCQAKTs+QcJy5awbCBu2TBtiJgJEbOAmAlE4haCMQudERPtoTi2todR3RpGWyiu+i6hDGBoAtcePixy5KjCBp9LPw7ABtWZiNIByzKlu9Micesfv3m/2vdRVatQHYbSQ57PwPhBWRhdHMDQfB8KAprM8Wq2z60Jj64Lt+4WtrTRHQ/JjmiX3R7tRHc8KIPxkAjGQ1rIjIiwGUHYjCBiRhG2oohZMUgpPy/I2Pm/AUgpoQkBl+6CW3PBrbvh0V3w6B74DA/8hg8Bl9/O9WTLAk+uzPXkiBx3lhZw+QQARK24jJqWDMUsdIal1tRtYWtbGJuag1jXGEQHV3uhvXTyhBL7yllDg16Xfg6AN1XnIRroWJYpXYmYad8aNe0f3/HaOt/6pqDqPDTADM33YkppDkYU+lGa60Z+QFhZHkMEXB7Nkhaaw21yW3e9rOnapjWH29Ae7URbtANtkQ60RzsRsaKqf4TPeXUPcj3ZyPPkIM+TgyJfPkoDg+yK7FIMDpSIQm+eAICwGbNDMUt2hSG2d8S15XWd+Ky6jUWa/sfEwVn48Uljwh5Dv8HQxB9U5yEayFiWKR25QjHrydZQ7IzbXlkXaA7yQD4nEwDGlgQwqzIf4wcH5KAc3c7yGLoudNQFG+2azm1yc1etXh9sQl2wEXXBJnTHM+/NVbYrgEGBIgz2F2NwoFiOyq20RuZWaMX+Ai1qxmVHJG7Xtdv6moZuzKtpw+bWMLikorOV5npx71fGhQJu/cGeA//4iCDaDZZlSjeBUMx6dX1j90F3v7HBHzF5IJ/TlOd6cdSoQhxQ5kdprsvO8Xq1UDyMzZ211prWjVpVx1ZR3bkV9cGmnSZBOJcmNJQGBmF4TjmG5w6VY/NHyGE5ZVrA5Ud7OGxvaze1FduD+HBTC7Z3DJzRdEqNXK+Bn31lXLAky/Oy361fAoAT5Il2wbJM6aQ4FLPe/Wxz26jffFDttTgslvFcusAhlfmYVZmPkcUuq8Dv1gAp1rVV24sbV2rr26tR3bEVITOsOmra8Rs+jMkfjvEFo+S04glyRG6FZkkpO8Jxq6Ylri/c0iHe29CCqMU3pJnOY2j40QmjQ+NKspb43frJ4EoZRF/AskzpYng4bn348sqGkqfm17pVh6H+oQE4clQBjh1bZA8rNGS226u3RTvkypZ19rKmtfqa1g3YHmxUHTMjCQiUZQ3G+IKROKBorDWxcKwo8ORo7ZGotbExrr21rknMq2lXHZP6iSaA64+ojBw5snCrz63PBrBddSaigYJlmdLBlEjcevfP82tzX17ZoKsOQ8lVkuXG6QcMwoyKgFWc5dW740E5v36ZvbBhub66dWNGzi9OFznuLEwpGo+Zg6dY00smaW7dLVqCMWtZbUh/dVUjato4op9pzps2xDx/Wmmb16XPAbBKdR6igYBlmQa6OZG49dKv368OfMyl4TKCJoBDhufjhHGFGFnssX2GS1vZst6eW7dYW9y4Eo3hFtURqRelgUGYVjIBhwyebo4vHGVETdPe3BIT/13dLD7c1Ko6HiXJnNGF8vojK4NeQz8VwAeq8xCpxrJMA5Yt5XnRuP3nu95Y71u+vUt1HNoPHkPglAmDcNToXFme5xOtkTZ8tG2BXNi4Qqxrq4ItOS823ehCx9j84ThkyAz7qPKZwqd7RV1n3PpgQ7v+yqp6REy+tqSzKWU5uOOE0aGetZj/qzoPkUosyzQgxS37hnDcvveHL6/xVbfyo950lOszcPaUIZhVmWUVZ3n1rV118t2tn2Bu3RLB0ePMMyy7DIeVzrDnlB+CAl+e1twdM99b3248t6wOMYuvM+lo3KAs3H3y2LDfrX8NwH9U5yFShWWZBpyYaX+/K2re/t0XV/sburiGcjoxNODsKUNw7Lg8qyjg1Ws6t1lvbflY+7RukWiP8gB7pxjkL8KhQ6bLOUMPleVZg7UtbSH7+WXN2vsb+SYp3Ywo9OPnXxkX9rn0b+ia+KvqPEQqsCzTgBI17R93RuK3fueF1f6WIJf7TBcHVeTh/OmD7OGFXq0p3Gq/Wv2u+Hj7QhZkQpE3H3OGHiJPrpwjfIZPrqoL4a8LtouqFn5ilC6G5nnxi9MnhHwu7dsuXXtCdR6iVGNZpoFCROLWT9vD5o23vLja3xZiUR7oSrLcuOzgcjm13Cd1XWjvbPnEerPmI72ma5vqaDRAjc6rxPHDjrRnlx2sBWOm/WlVt/a3hdvQHbNUR6M9GJLjwf2nTwgF3PodbkP7leo8RKnEskwDgYjErV+0huLX3PLC6kBHxFSdh77EqRNLcNoBhVZxlldf1rTGenXze/rixpU8SI/2miF0zBh0AE6unGMfUDRWq20P2c8uadI+4IoaA1pxlhu/PH1CKNtr/MJjaD9RnYcoVViWSTURjlsPNgdjV9z64ppAJ4vygFTgd+HKWUPlgcMCCJohPL/xDXxQ+5no4hrItJ9y3Fk4ruJwefrI44UhXPbiLSHxp3lbBadhDUz5fhfuP318KN/neszr0r8LcE95ynwsy6SSCMetxxu7ohfd+tKaQHeUH8UONDMrcnHJwaVmaa7HWN2ywXpmw2v68ua1qmNRBhIQmFY8AWeNPtEaXzBKr2mJWI9/slVf18g3ZANNjtfAL04bHyzOcv/e69K/DRZmynAsy6SKFo5Zf6jrjJz3vZfXBkKcszignD5pEM6cWmhleQz9jZqPrJer3tYbQs2qY5FDDPIX4bQRx9jHDztSawvF7b8taNDe38gpGgNJtsfAA2dOCBYGXA96DP021XmI+hPLMqkgwnHr97XtkQt+8PKaQDjOua4DgSaArx1YhpMm5suwGcIzG17Fe7WfiajF5ftIDY/uxtFDD8EFY04DpMt+bmmz9uKKBtWxqEeez8ADZ04M5flcP/MY2k9V5yHqLyzLlHKRuPWrhq7o1be8uIYjygOAoQlcOWsojh6bKxtCTXhqzXNiceNK1bGIPqdB4LDSA3Hx+DNllisb/13VLv66sBY2X76UK/S78OuzJoayPcbtbkN7QHUeov7AskwpFTWt29tC8e/f/PxqPw/mU8trCFx92DAcPjJHbu6sxVNrnhOrWtarjkX0pQ4sOQAXjz9TDvYPwocbOsTvP9uCKLfWVqo4y41fnzkxlOXRb3Hp2mOq8xAlG8sypUzcsm/ojJg/v+k/q/ytXEdZmSyPjmsPH4aDK7OxpnWj/fSa/2gb2jerjkW0TyYUjMLF4860R+UP1+Zv7pIPf7hZcEqXOoNzPPj1mRNCAbdxna6JP6vOQ5RMLMuUEpYtL+uOmr+9+flVPm5hrYbX0HDj7Eo5c1i2WNq02v7Lmuc1biBC6W54zlBcOfE8e2zBSG3B5i75EEuzMuV5XvzqjAlhv1u/XBPiX6rzECULyzL1O1vKs0Mx6y/feWG1r7Y9ojqO43gMgW8dOVzOqszCuvZq+/cr/sFd9ijjjMsfiasPuNAqyxqsvbmmTfx+7lbVkRxpWIEP958+PhxwGxcAeEl1HqJkYFmm/nZCKGY9/72X1viqWkKqszjO12aU4bTJBbIx3Gw/vvxv+qrWDaojEfWr6SWTcPUBF8psV7b86/xG7dXVjaojOc6oogDuPW1cyOfSjwUwV3Ueov3Fskz96bBI3HrzR6+u869p6FadxVGOGFmAbxw2xDZlRDyx4u9iXv1S1ZGIUkZA4PDSA3HlpPNhWy774fdrtcXbOlXHcpQZQ3Pxw+NGdXpd+kwA61TnIdofLMvUX8ZG4tb8n765MWdxbYfqLI5RnufBD44fbhcENO3JVc/Kd7Z+ImzJ+ZvkTLrQccKwI+SlE84RW1qj9s/frNZaeHBxyhw3tsi++rBhTT6XPhVAveo8RH3Fskz9oSQct5Y98UlNyVvrmjXVYZzApQl8++hKOXNYjnix6i357/WvcjMRoh4Blx+Xjj/LPnroYdoHG9rwyEc1XKM5Rb46vTR+1pQh1X63fiCALtV5iPqCZZmSzR+KWfNeXtkw9ukFtS7VYZzgKxNL8LWZg+XG9ir5yLKntfpQk+pIRAPSsOwy3DD1MjkkMAR//qxevLGWfyupcMNRwyNHjChY6HfrxwDgu3hKOyzLlEx6KGa9unBL+5H3vbPJpzpMphtZ6Mf3jx9me92W9tDSp7CwYbnqSERp4bAhM3DtlIvRHYH1i7dr9I3NPPi4P2kCuPOkMaHxg7Jf9bv18wGweFBaYVmmpAnHrcdqWsOXfO+lNX6Tn3H2G4+h4QfHjpAHlAXEcxtft5/d8JoWt7kbItG+8OhunDf6FPv0kcdry7d12z9/a5MWs/i81V88hoZfnj4+WJrrfdzr0m9RnYdoX7AsU1LETPvbraH43Tc+t9LfHbNUx8lYp0wswaUzB8v17ZvkQ0uf0prCLaojEaW10sAgfHfGN6zB/kHi4Q+2aZ9Ut6qOlLFyvAYeOntiKM/n+q5L136rOg/R3mJZpmQ4syti/u2G51b6Grs5Ha0/5Hh0/PQrY8xBOYbxyNKn8NH2BaojEWUMAYETK4+SV0w8V2xsjFh3/nejHjG5ikx/GJLjwUNnTwr73fopAN5TnYdob7As0/46KBK33v/eS2v9G5uDqrNkpBPHF+PKQ4bI5c1r7IeW/lnvjHHNaqL+UOTNx83Tr7RG5lZqT3yyXby7np/c9IcpZTm444TRnV6XPg1Aleo8RHvCskz7Y3Akbq28/91NhZ9tbledJeP43TruOnmkPTTfpT249EnMrVuiOhKRIxxVdrC8dsrFYktr1Lrj1U16KM6pZcl22qRB1iUHldf43PpUcEk5GuBYlqmv3KGYNe+llfUT/7JgG5eIS7LZowpx7RFlcnHTCvnosr9o3XGO2hOlUo47CzdMvcw6oHC89vAHteKjKs5lTrabZw+PHDq84CO/Wz8RAOe90IDFskx9Eo5bf1xT333BHa+t8/MRlDy6JvCj40fICUO84leL/4AFXA6OSKnZ5bPkdVMuFstrg/Y9b2zS2OiSx9AEfnnGhODQPO8jXpf+fdV5iHrDskz7zLTlVa3B2G+ufWaFPxznS0eyjCry486TR8ja4Fbct/Bx0R7tVB2JiAAM8hfhhwddZxd4iuSdr1VxXeYkyvUa+O25B4RyfMYVmhD/Up2HaHdYlmlfzQrHrHdv/M8q37aOiOosGeOymeX4ygGF+NvaF+0XNr2pSa7ZTzSgaELDV8eeZp058nj91ZWt8k/ztgrVmTLFiEI/7j99fMjr0o8AsFh1HqJdsSzTvhgSiVsr73tnU8H8mnbVWTJCrtfAz08bbfm9ce2eeQ+LTR1bVEcioi8xLn8kbpt5neyOaNb3X9pgtIe5IVAyHDo8X35nzogWr0ufCKBRdR6inbEs097yhGLW/OeX14//+yIe0JcMR48uxDVHlMn59UvtR5Y9rUesqOpIRLQX/IYPN0y91JpWcoD2i7dqxMKtHaojZYTLZpbHTpk4aJHfrR8BgEuQ0IDBskx7JRSznlpV33XOT/67ngf0JcH3jx1hHzgsS3t46VPyw23z+XEuURo6cdiR8uuTvipeXN6MpxfUqo6T9jQB/OK08cHhhf6HvS79B6rzEO3Askx7ZEt5UUsw9rtr/s0D+vZXllvDr84aaxlGVLtj7gNie7BBdSQi2g9j8objx7NuRE2Lad/2ygbN5kvqfsnzGXjsvMmhHK9xDoD/qs5DBADa3pxJCHGZEGKREKJLCNEmhFgihHhgl/PInn+H7HL6pJ7TZ+902vs7nd8UQmwSQvxaCJGTjB9qp9txCyHuEEJsFEKEe77+RAjh2ek8Bwkhnuz5XkgIsU4I8WMhhHcvrvvfQoiqnutuEkL8VwgxY5fzjRNCzBNCdAgh/imEyNrl+0cKIbbtevoAMjpm2k/c9foGFuX9NH5QFv5w4US5NbQB1793B4syUQZY316Na979EVzeFvzpool2cZZbdaS01h42cc8b6/2RuPVPABWq8xABe1GWhRA/APAHAG8AOAvAJQBeBHBaLxf50V7e9nsADgEwG8AjAK4C8Ke9vOzeuhfA9wH8FsDJAB4DcCuAX+x0nvMBjARwX895HgXwbQB/28N16wAkgJ8DOAWJ/H4A7wohRux0vj8D2AjgPAATAPxwxzeEEBqA3wD4gZRyIO5h7AnFrFeenLfVV9XCpZL2x2mTBuGeU0fi3xtekj+d/yjnJxNlkM5YN7738X3ax3Uf47fnjZcHD8tVHSmtrarvxj8Wb/eHYtZLAHiMDCm3x2kYQohtAF6QUl63y+lC7nRhIYQE8D4S5Xe6lHJJz+mTAKwAMEdK+X7Pae8DaJZSnrPT5X8E4G4AJVLKpv39wXqusx7A36SU39nptAcAXCSlHNTz/8W73p4Q4hsAngBQKaWs2YfbywLQgkT5faDn/7vQ8zMJIc4HcIuU8qCe818F4OsAZskBOB8mHLceX7m96+I7X1/vV50lnX3v2BFyRkVA/Gz+o1jWvEZ1HCLqR4cMmY5vT78Sr65owZPzt6mOk7YEgJ+eOi40tiTwuNelf2ePFyDqR3szDSMPQP2uJ/ZS7v4DYDWA2/qQZVHP18o+XLY3LgC7HqbcjsTfIQCgl2K+pOdryT7eXhBABMCOz+F2fA33fA3tOK1nysndAG4ciEUZwOmRuH3xL9/dxKLcRx5Dw2PnjbdGDbJww/t3sigTOcDcusW4+YO7MWecH3efPEry6N2+kQDufXujP2bZ3wRwouo85Gx7U5YXA/iWEOJSIUThHs4rAfwMwFlCiAn7mKWy5+v/FPP98AcAVwshDhNCZAkhjgBwDRLTPr7MoUjsU79uTzcgEgwhxGAkpndYAP4BAFLKVgDVSNx/BQC+AWBhz0VvB/C2lPKzPvxc/a0iErf+cs+bG/zdMa7e0xfFWW784cIJdnO8Rnzr/TtFQ6hZdSQiSpHa7np86/07kZPdiSe+Ot72u/bq8CDaRWfExD1vbtwxf7lMdR5yrr35C74OQDcSc2+bhBCrhBB3fcnBeP8EUAVgT8u+7CiZnp4SexsSRTKZ6+98H8BzAD5GYjrEhwD+I6W860tCDe7J8hcp5d7sN/w9AHEAdQAuBXDyLlM3ruu5vhYAYwH8RAgxCsCVPfkGGiMUs1785+LtvrUNA3Ea9cA3cVAWHj13nJxbP0/eOfc3WtSKqY5ERCnWGevGLR/9XFR3rcMfL5poDyvwqY6UllbVdeG5ZXX+YMx8Bnu5KAFRsu3xgSelXA5gPBIH9P0WiSkMtwNYuLsVHKSUFhIH1n1VCDHyS676LCRKZgSJErsZibnEu52SIITQe8r1rv++7FOu7wL4GoBvATgKwA0ALhJC7LYsCyHcAP6NxJuDm7/kenf2ZwAHIXH/LALwys6j6lLK/yIxnWMsgPFSyi0AHgDwayllrRDiOiHElp5/1+7lbfabSNz62cbm4Jhnl9YZqrOko2PHFOGuU0fi7+tekI8t/6tuc9tqIscybRP3LnxM++/mt/GrM8bII0YUqI6Ulv65eLuroTM6OW7Z31KdhZypT+ssCyGuRGKKw01Sygd7TpMAviWlfKSndG4E8DqAh7D7A/wsJEZlTQA1Usq2Pdzm+0gU3l19fr27nL8IwHYA10kpf7/T6VcjMQ2jTErZuNPpAonpE8cBOExKuXaPd8T/3qYBYBWAeVLKS3o5z7EA/ghgHIAxSKwKcmjPt+cCOKrnDYoKR3VFzNeu/tdyf0eEW7juq8sPLpenTioUv1r8e8ytW7LnCxCRY8wpP0ReN+Vi8e8lTfjX4u2q46SdITkePHLOpJDXpR8IgAeAUEr1afRQSvlHIcQvkCh8u/t+TAhxP4BfInHQ3+60SSkX9vK93bkaQPZuTu9tXvEIJA7wW7rL6UuQ+LmH4Yv7z/8awOkAjutLUQYAKaUphFjRc9v/o6dM/wbArVLKcM/a0+/uuD0hxDtIvCFQUZazw3HrX796bxOLch/cfsIoa1KpR//hp/djfVu16jhENMC8VztXNIZbcMfBN6DAZ8jHPtnCY//2QV1nFH+Yu8V7xayK//jd+mQkPpkmSom9WWf5f1aEEEIUA8gF8GW7KvweQBsS6xrvNynlOinlwt386+rlIjvmDU/f5fQdm4Zs3nFCz1rS3wLwNSnlx33N2LORyXQkDurbnW8i8SbhXzudtvNqEwHstFJHKoVi1sOfVrflLtiy6+IhtCf3nz7WGlkitRs/uItFmYh6taplPb794d04fLRf3HrMcM7R2kf/XdOkrW/qrojErV6POyLqD3szWX6FEOJ3QohzenabuxjA20gsg/ZUbxeSUkaQmJs7JzlR942UsgHACwDuE0LcKISYI4S4GYn51M/sWDJOCHEhEit4PA1gmxBi1k7/indcnxDij0KIjTv9/1eFEE8LIS4UQswWQnwVwFsAhiDxc3+BECIfwI8B3LjTyR8COEYIcYUQ4goARwP4IKl3xN45IWbZ5z7+Sc2X7lpIXyQAPHj2WCvLH9Ru/OAurnhBRHu0rbsBN71/F8aXauInJ49iYd5Hv3y3ym9JeSOAWaqzkHPsTVm+C4ll3R4C8CYSawOvAjBTSrmnYbTfAmjdn4D76VIk5lbfAOA1JFameAKJlSh2OL7n62VIzBne+d8pO51PxxenrawDUIhEMX4DiZ38tgM4cMeGLLv4CYCXpJSLd5zQc75bAfy0598tUsplffg590deJG797f53NvlDXCZur2kCePTc8TaMdu2Wj34mOmNcOYSI9k5zpA03fXAXSnLDuP+MMTbnY+y9tlAcv3m/2heOW88h8WksUb/r0wF+lDlCMetfH2xsOf2RjzZ7VGdJFy5N4JHzxtsdZh3umPsAl4Yjoj7xGV789NBbpG4XyJueW6fZfDnea7ceMzI8syLvnz63foXqLJT5uGahs50Wjlun/mHuFhblveQ1BB6/YLzdGK3Bjz79JYsyEfVZ2IzgB5/8QoRkvfjteeNtQ+MY89565KPNvqhlnw/u7kcpwLLsXEWRuPXUfW9v9EdMW3WWtBBwa3j8/Al2Vdc6/OSzB7W4zVVDiGj/RK0Ybp/7gKiPVovHLxhvu3UW5r0Rilm4/51N/nDcehrA/+z5QJRMLMsOFYpZf3pjbZNvVT3n2u6NLI+Ox8+fYC9vXSbuXfCYZknO7yai5DBtE3fPe1jUdG/A4+ezMO+tpds6Mb+mPSsct+5TnYUyG8uyM50ejJlH/3neVk6/2AsBt47fnjfOXtS0GL9e8kfBXfmIKNlsaePnCx7Ttoaq8cQFE2yPzpfnvfH4JzU+W8rLkdhJl6hf8K/RebIjceuPv3q3KhCzWPr2xOfS8Nj54+2VrSvx0NI/8++FiPqNJS3cM+9hbXuoRj7x1fGW1+AI8550Rkw89nGNNxSz/o7ERmRESccXf4cJx637P61u86+o620vF9rB70rMUV7fsU7+atHvNckRZSLqZ6a0cOe8B/WGcC2euGCixSkZe/behhZR1RIaErfsW1RnoczEsuwsB9m2vOR3n27xqQ4y0CVGlCdYNd1V8t4Fj+mcekFEqWLaJu6Y+2u9Kbodj5473uIL9Z79+r2qgGXL2wGMVJ2FMg//Bp3DCMWsvz32SY23K8pVHL6MoQGPnjveqg9vxd3zH9Z5MB8RpVrMjuNHn/5KD9rN4jdnj+OSRXtQ3xXF3xdtdwdj5l+Q2GCVKGlYlh3CtOxvbW4Nlb63oYVPInvw4NnjrfZ4g/jxZ7/RTS4PR0SKRK0Yfvjp/Zrm6hR3cWvsPXp+eZ3eFopPtqW8WHUWyiwsy85Qakl5z4MfVHNr0D2455RRttQ7tNu44QgRDQBhM4LbPr1fVBRB3HDkMBbmL2FL4P53NgXilv0IgELVeShzsCw7QDBmPvbSigZXbXtEdZQB7brDK2RFodBu+/R+EbGiquMQEQEAOmPd+P7H9+HQkVni/GlDVMcZ0DY2h/Du+hY3116mZGJZznzHxUx57D8Wb+eSOl/irMmDceSYHPH9T36Bzhg3aiGigaUh1IzbPv0lzp1ejKNHc9D0yzw1v9YjJS4EcIDqLJQZWJYzmysUs/7w8IfV/ii3tO7VoZV5uOigQbhj7gOoCzaqjkNEtFtVHVvw0/mP4rojy3FAabbqOANWV9TE0/O3eoIx8w/gwX6UBCzLGcyy5Tc2t4YK59W0q44yYI0q8uPbxwzD/Yt+h/Vt1arjEBF9qaVNq/HosqflHSeOkEPzvKrjDFivrm7UOiPmRABnqs5C6Y9lOXPlmbb9899+tJkH9fWi0G/gp18ZJZ9a/aycV79UdRwior3yXu1n4pkNr8j7zxgtsz266jgDki2Bhz/cHAjHrccA8F0F7ReW5QwViVt3fbSp1ahuDauOMiC5NIFfnz3OfnvLR/KV6nf5MR0RpZVnNrymfVa/SD50zjibL+S7t2xbJ1bWdWXFLPtW1VkovfFvLDONAvD1P8+r5U59vfjVWWPt6q5N+OOqf/FvgIjS0qPLntY6zCbcf8ZY7pzUi8c/rvFLie8DKFOdhdIXi0IGCsbMR/69pM7VFo6rjjIgffeYEXbAGxX3LXxck9zGmojSlCkt3Dn3N9rgPKHddFQlj+LejfquKF5aUW+EYtaDqrNQ+mJZzjxHxS15xPPL6wzVQQaic6YOwcxhAe32uQ+IsMl1p4kovXXEunDH3F+LI0blaseO4ZJyu/PPxdtdpi1PAjBLdRZKTyzLmUULxawnnvikxh+zOGK6qwOGZOOrM0pw74LHuEQcEWWMqo4teHDJk/KbR5RjWD5n3+0qYtp4ct4WXzBqPgIuJUd9wLKcQWwpL6nvipZ9uKlVdZQBJ8ut4bYTKu1/rX/FXty0SnUcIqKk+mj7AvFq9bvWz04bZbt19sFdvb2uWYTi1jgAx6nOQumHZTlzeGOm/atHP9qcpTrIQPSLM8Zaq1rXymc2vMbHPBFlpKdWP6dv7qzGL04fw/nLu7Al8Ie5WwLBmPkQOLpM+4jFIUNYtrx6TUO3Z20Dt2re1fVHVsDlCmn3L/odFyQloowlIfGzBb/VCrOldsWsoarjDDifVLWhNRgvA3C26iyUXliWM4M/btl3/umzrdyAZBcHVeRi9ug83DH31yJqxVTHISLqVyEzjLvmPYRTJhZgSmmO6jgDigTwu0+3ZIVi1q8B8CB42mt8sGSAuGVfv2x7p7uqJaQ6yoCS5dFxyzHD5MNLn0J9qIkfu1Gf6UJHib8QQ/zFKPYXwm944TW88BveeMDlNwMun+U3fLbP8Eq/ywev7hEe3a3rQhOmtOy4bUrLtmTcjsuYFUfQDCMYD2ldsaDRFQu6uuNBoyXShoZgM+pDzWiNtIPLGlJfbWyvwV/WPG//4PjTcfnfVmnhOGdl7LC4tgNb28N5o4sDl2hC/El1HkoPQko+Iae57KhpbbvpP6uzt7Rxt76dPXzOWHtzaDUeWPwHfoJCe+TR3RjsL8aQQAmGBIpRnjUkXJFTGhvsL9Zz3Fm+qBVrM6W1WYPYaGhGi1t3tWhC6waw41/XTv+94/8tAB4A7p6vHiS23s3Z6V+uaZsFYTM6RkKOcmuuckMzsjqinaG6UJO9raveU9td720INaM+1IS6YCO47CHtjZ/MutnK1Stw43NrOQVtJ+MHZeGeU8a2eF16OQD+MdEecWQ5zcUs++aFWzp0FuUvuuLgcmT5TO3RuU+rjkIDkAaBYTnlmFA4CpOLxndPLBwtsl0BT8SK1lu2XeXWXSu9hmcNgE09/zb7NV+/zeMxNAPZ7i88HfsLffmVhb78EZMKxwyPmtGxUTs+QYMY4TU8pa2RjsjKlnXGiuZ1vrVtm1DbVc+RaPof9y/6nf74MffgkoPK8PSCbarjDBhrGrqxpqHbe8CQ7GsNXXtAdR4a+DiynN7yoqZde/2zKwPbO/jmeIcJg7Jw96kjcctHP8PmzlrVcWgA8BlejMkfjokFo61pJRO7R+RW+EzbbALEBwGX720AnwLYACAdPq92AZgM4JDuWPBYTWiHaELL3dheE13WvCZrTetGbV1bFUefCQAwLn8k7jn0O/jhyxuxrjGoOs6AUVngwwNnTujyGHoZEp8CEfWKZTmNxUz7Z59Ut970y3eruAp9D7cu8NTFk+Rf1z6H1za/x3nKDmVoBqYUjcPBg6dGpxZPiBb7C70RM7LWo3vecuuuD5Aoxy2qcybRYACzYlbsiKgVO9ZreMe1hNuic+sWez/evtC1vq2aI88Ods7ok+wzR5yMS/6yUjPT4e1gitx2/KjQQRV5d7l07T7VWWhgY1lOX0VR06655t/L/Q1dXOVhh1+cPtoOi62487MHOU/ZYQIuPw4aNBlHls3snlI83hWz4usDLt/fNaG9D2AxACf9obgBTItZ8dPjdvxrUsqij7cv1D7avsCzsnk9LGmpzkcpJCBw3+Hft2ORQnnbKxs4f7lHZYEPvzpjQofXpQ8G5y7Tl2BZTlORuHX/h5tar3/wg2qv6iwDxVcmluCimUW46u0fojvOjxudIGD4MGvIdBxbcVjXuPwR7ogV+yTbHfgbgFcAcE/z/zfWtK2zI2bkYkMzKufVL7U/3Dbfv6RxFWJ2XHU2SoFiXwF+e/TdeOzDbXhnQyZ9qLJ/7jllbPfk0pzv6Zr4reosNHCxLKennKhpb7/m38sDHFVOKAq48dj54+QvFj0hFjYsVx2H+pFbc2HWkGk4tuKw7kmFY11RK/ZRtjvwRyQKMnfl2bNyKeUZ3fHQZR7dNWlZ89r4mzUfZc2vX8YR5wx3XMXh8sqJF+CSp1eJqMX5GAAwLrEyRqPPpZcBMFXnoYGJZTkNxS371vk17T/+2Vsb/aqzDBRPXDDB3NS1Cvcv+h1XeMlQxb4CnDr86PhJlbNNS9pLs92BJwC8AKBDcbR0Vgjg9O5Y8AYAY17b/L7rv5s/MJrCHHnMVD899BZLxAeLH7y8gVPVejxwxoTusYOyrgHwV9VZaGBiWU4/nkjcqvvui2vyuQlJwnnThuDMqfnyqrd/IIJx3ieZZmLhGJw96sTglOLxwpb20z7D+wASK1dQck0Mm5HrdaFdsratyv7PxjeyFjWs4IGBGabAm4fHj74Hv3mvFp9Ut6mOMyBMLcvBbceP3uJ368ORHiviUIpxFC79XLixOeRiUU7I9xk4b1qxvG/REyzKGcSju3FU2cE4Z/RJ3XmenA6v4blXE9pT4BJP/WmVz/BeA+A7k4vGnTcyt+KHYTNa+syG1/zvbPlERKyo6nyUBK2Rdjy67C/yW0ddjPk17SJu883Q0m2daOqOFQwr8J2GxKdVRF/AkeX0ooVi1uZ73twwdNm2TtVZBoSHzxlnbQ2vwf2LfscjvDNAYqrFMbGTh8+2Lduel+X2/wzA2+BojwoCwOHd8dBtutCOemPzh9p/Nr3hbo20q85FSXDnrJtsxErFj1/bxCU2ARxSmY+bZw9fE/AYEwF+nEJfxDlL6eXU5mAsn0U54dSJJSjO1vTfLv8ri3KaK/Dm4capl0ceP+anoVOGz/mDz/AekOX2zwbwJliUVZEAPspy+U/0Gd4JJ1Ye9cffH/Oz8OUTzo0FXDxcIt39evEftQmD/WJ6WY7qKAPCZ5vb0B2zKgAcozoLDTwcWU4jwai57KEPN0/+uKpVdRTl/C4NT108Sd6/6AmxgKtfpC2/4cO5Y06OnTb8GEsCT3gNz90A+AAfuIaG4pGfCYFznln/muvFqrf0qMUVedLV8RVHyK+NOw+XPL1SsAkAR48uxNWHDZuf5TEOVp2FBhaOLKePwyKmPfLTavYIALj9xJH2qpb1kkU5Pbk0A2eOPN768/H3h0+pnPOsx/CM9Rqem8GiPNBt9bu8F/sM79SzR5/4+lPH/zJ8yvCjbUPww5109NaWj0V7rBnXHF6hOsqA8OGmVgA4AMAkxVFogGFZThPdUfOufy7e7uexGMC08hyMKfFrjyx7mo/fNKNB4Jihh8o/H39/6IKxX3nX7/Id6Hf5LgKwVXU22ifrAi7/qVnuwBGXjD/z0yePvz80u3wWNHD6azqRkHhg8R/FMWMLMDjHozqOcqYt8dLKBlcoZt2iOgsNLJyGkR5GhmLWioueXuyLWfx9/eXiifZ/ql7BC5veZFlOIwcNmoxvHPDV7hx31oaAy/8tAJ+ozkRJMzsYDz3cGese/vjyvwUWNa5UnYf2wTcmfdWeUjBTXvvvtY7/iCDf58KfLpwScRtaKQCurUcAOLKcFiJx64bX1zTqLMrAlbOGImoHxUtVb/OxmyaKfYW497Bbg9+d8Y3NQwIlFwZc/hlgUc407wdc/slDAiUXff+ga+q/f+A14Rx3lupMtJeeXvMfLc8v9FMmlKiOolxbOI55Ne22adtfV52FBg4WjoHPJ4S44tVVjW7VQVTL9Ro4aWIBfrPkT8KWXCBhoBMQOLlytv3bo+8Kj8kfca/f5RsN4GVwWaZMJQG86DO8o2YMmvTH3x/78/CRZTP5u04DESuKR5Y9jUsPHiy9BmvB88vr/HFL3gLA8SPtlMC/ioHv/LUN3ajv4oYAPzphhLW4cYW1qpWbtw10g/3FuP+IHwQvm3DOKp/hneHWXfcAMFXnopQI+gzvtwIu/5zrp15ac/ch3w4VevNVZ6I9mFu3GBs7NsvbThhpqc6i2rrGIBq6oj4Ap6jOQgMDy/IA1x01v/f88jrHf545rTwHI4p8+uPL/853+gOYBoHTRx5nPTLnJ+ERuRU/8bt80wCsUZ2LlJjnN7xjJxSOfuDxY+4JnzjsKFvwAMAB7ZFlT2kThwT04YU+1VGUe3ZpXXZ31Pyh6hw0MLAsD2wHxS05dNHWDtU5lLtp9lDrX+tfsduivC8GqvKswfj1UbcHLxp7+jKv4Zni1l33A3D8KJXDxTy6+3af4T3o8onnrr7/iB8EhwQ4L3agqgs24u2aj61bjq50/N/tR4ll5CYDmKA4Cg0ALMsDWDBmfufFFfVepy8Xd/oBg+AybO3FTW/x8ToAaULDOaNPMh886o7QsJyyH/pdvoMAcK4M7WxVwOWbOjJv2B0Pz74zdPqI4xxfxgaqv659QR+U49KdvrOfaUu8nFhG7ruqs5B6LB8DV6Ghaae/sbbJ8dMOzp9RbP9p1b8Rs+Oqo9Au8jw5uP/wHwTPG33KIo/hmWRoxkPg9tS0e5ZLMx7wGp4DLhp3+obbZl4X9upc23eg6YoH8c91L9vXH1Xu+Dc0r65uNAxNnA8goDoLqcWyPECZtn3FvM1tdmfE2cdEXXJQGYLxbvHe1rmc7DjATCwcg8eOvidcmVv+oN/lOwxAtepMlBaq/C7ftKnFE/7z6Jy7QmVZg1TnoV28VPW25nHZ+vFji1RHUaotFMfq+i4LwDmqs5BaLMsDkxYz5bdfWFHvVx1EJV0Ap0wqkI+v+JuwudrYgHL2qBPNu2bd1JXtDpzl0d23gXOTad9EfIb3a4W+/Jt+c9QdoUOGTFOdh3YSt038buU/cOmswbbTRyleWdWY1RU1b1Sdg9RiWR6YDu+MmFnrGoOqcyj1zcOGobZ7u1zM3cAGDK/uwe0Hfyt0/phT13kMzwEAXledidKXoem/9xne2d+ZflXT5RPOiWmCL0kDxUfbFqAj1i6+OqNUdRSlFmxphy7EeACjVGchdfjMNACFYtY3Xlvd4OhRZb9Lw5yxeXhs+d/4GB0gSnyFeHD2j4MHFI170e/yzQBQozoTZYQFXsMz4aTK2QvvPezWIHf+GxgkJH67/C/ijClF0tCcO75s2hJvr2vSYiZ39HMyFpGBx2do4qz3NrQ4+ndzw1GVWNK40t7UwT42EEwqHIOH5twZLvEX3u43vBcB4C45lEzNfpfvyBG5FY/99ui7Q2PyhqvOQwBWtaxHdccWXDlrqOooSr2+tsltS3kVuKOfYzm6kA1QZ6xvCpqtIeeu/BBwazhoWA6eXP0sH58DwAnDjrTunHVTV5bLf7pLM34NbldN/cPyGp7v5nlyLvrZYd8NHlY6g4+zAeBPq/4tjh2XL926c0eXa1rDaOyOuQAcqzoLqcEyMsB0Rc3rXlvdmK06h0o3HFUpFzYst+uCjaqjON65o082r5p0QZPX8MwA8JbqPOQIL3gNz+E3T7uy/aTK2VyGULH17dXY2F4trzl8mOooSr2ysiGrO2perzoHqcGyPLAMceviwM82t6nOoUyWW8eBFdnir2uf52NTsUvHnxU7b8wp272G50BwkxFKraVew3PgFRPPa7ho3OnO/ZhtAPAbPjSEmsXhw7PgdTn3afmDjS3CrWvHAShQnYVSz7mP/AHIsuXXPq1us6OmcwdTvnXUMLmseY1d212vOoqjffOAC6OnjDhms8/wHghgm+o85EhVPsMz7YyRx1dfP+WSqIBzpwGoEDB8uGjc6fZTJ/wSh3uG2e7aWvv6w4c5dmpMd8zCwq3tppTyQtVZKPVYlgcOETGta19f0+RTHUSVQM+o8l/WcFRZFQ0CN0+7InL00EPX+Q3vwQCaVGciR2vwGd6DjiybueKWGVdFuLRc/wu4/Lh43Jn2Uyf8EmdkT5XWD29C/OIz9OiDP9OOGpolfIZzfwevr2kKdMesb6rOQann3Ef9wDMlbsmSVXVdqnMoc+NRlXJ160Zrc2et6iiOpAsd3zvomvAhQ6Yv7dmRr111JiIAnX6X76iDBk1e/IODrgnrggsS9IcsVyBRko+/H6dlHSDN79+I+KVn6vaSBQAAa80K2NUbrOuPrHTs6PLSbZ3QhRgJoFJ1FkotluUBIhK3rnxjbZPbqc9CWR4dMyqyxFOrn+MroQIuzcCPZ90QmlY84TO/yzcHQLfqTEQ7CfldvmOmFI3/7PaDvxU2NEN1noyR5Qrg0vFn238+/n6cljVJmt+7AfHLztLtZQv/57zRPz6iH1Hmh1MXxrBsiU+qWmHa8gLVWSi1WJYHBiGBCz7Y0OLYV4Drj6iUG9o3W1xXOfU8uhv3HHpLaFz+yHf9Lt+JACKqMxHtRsTv8p04oWDU+3fOujHEEeb9k+0K4LKeknyqf7w0b70O8cvO1u3li3q9jLV8EeT2rfKSmeUpTDqwvLuh2RuJW1eqzkGpxbI8MMwIRi1vTVtYdQ4lNADTKwL429oX+OqXYrrQceesm0Ijcoe+4nf5zgAQU52J6EvE/C7faWPyhs/99vQr+aauD3LcWbh8wrn2k8ffj1N846R5yzWIX36Obq9YsleXjz39hHbqiGzHHoW+MjFVsgzAGMVRKIVYlgeAqGmf9/7GFo/qHKqcO60U7dEOrGxZrzqK49w07fLIyNyKz3yJXfks1XmI9oLpd/lOP2jQ5A2XTjibb+72Uo47C1dOPM9+8rhf4GTPaGl+52rErzhXt1ct26frMed9BLcV1w4bkd9PSQc2WwIfbmzRTMs+T3UWSh2WZfWEZcuLPtrU6lIdRJWTJuZZ/17/qkNnwalz0bgz4gcPnlrld/lOA2CqzkO0D4J+l++YU4cf3XTK8Dl8k/clct3Z+Pqk860nj/sFTnSNlPGbr0b8yvN0e/WKvl2hbSP27F/l5VOKHTu6/OGmVk/EtC9TnYNSx7FzZAeQiXHLztvYHFSdQ4nJpdnwuzX9w23zVEdxlOMqDrfPGHlci8/wHg3AmQ8+SndNPsN75OUTzl3YGmnPn1u3d9MInCLPk4PzRp9inTDsCF3WbkH8pqtgr1uVlKlusf8+LwZf8g0xJMeDus5oMq4yrayq74IQKAMwEsAm1Xmo/3FkWbG4ZZ/3wSbnHth3+cGl9itV79pxmwObqTK9eCKuPuDCbp/hnQ2gQXUeov1Q5TU8x35n+lXBCQWjVGcZEPI8Obh60letPx57H47XKhC/8euIXXWBbq9blbwb6epE/MO37WtmlTtyASdbAp9UtcGy5Tmqs1BqsCwrFjXtr320qdWtOocKuV4Dwwp92svV7/BxmCIjcivwg5nXhr2G52QA61TnIUqCxV7Dc9ads24Kl2cNUZ1FmXxPLr55wIXWH4+9D8eJoYh/63LEvvFV3V6/ul9uL/bvv2jTBvuFoTlzBt0HG1u84bh1ueoclBosKWqNAjB4Tb0zl7S96pChWFi/3G6PdqqO4gjFvkLcc+h3Qh7dfTGAT1TnIUqiNz26+5p7D781VODNU50lpQq8ebh28tesPxx7L45FGeLfuhSxqy/U7Y1r+/V27c0bYW+psr92YFm/3s5AtaKuC4YmKgGUqs5C/Y9lWSHTlud+UtUqnPg5liaAmcOz5b83vMbHYAoEXH7ce9itIa/uuUMT2nOq8xAlm67pT/kM789/dth3gx498z+sK/Tm4brJF1u/P+bnONoahPh1lyJ29UW6vTF1qwrF/vmkduqILEce6GfZEku3dcYBnKg6C/U/FhWFwnHr4g83tXpV51DhpPElaAm3gpuQ9D8BgR8edG0o15P9tFt3/Up1HqL+4tHdPy3w5L5+7eSLM3YN5kJvPq6fcon1u2N/jjlmCeLXXIzYNRfrdlXql940P/0QHljaIZXOXEbu0+q2rK6Ieb7qHNT/WJbVKfLo2sieBc4d57TJBfaLVW85c7Jbip056nhrdF7lJq/huUF1FqJ+Jv0u3+WHlk5vnl0+K6M+tCvy5uNbUy61fnfszzA7VoT41Rchdu3Fur15o7pQtoXYc3+TV0xz5jJyi7a2w+PSjgSQ+R9lOBzLsjrHrarvipp2Rj2f75XB2R4UZ3m1j7YtUB0l443Oq8SFY08P96ylHFedhygFunyG9yvXTbk4UhoYpDrLfiv2FeCGqZdZTxz7MxwVzUf8Gxcidt0lul1TpToaACD+6vNiSJ5fKwo4b6uA9rCJ+s5oDMBhqrNQ/2JZVqQ7ap45t7otW3UOFS4/uFwuqF9uhUxnbu+dKj7Di9sP/lbIa3guB7BZdR6iFFrq0ozv3jHrhqBLS8+VOYt9hbhx6uXW40f/FEeGcxG/6gLErr9Mt7dUq472BbKzHeayhdblsyqcN/ID4OOq1kDUtM5QnYP6F8uyGsLQxPGLajtU51Bi6lA/Xt38blIWx6fe3TztirDP8P4LwLOqsxClmqEZvy3w5L539QEXptWuGSW+Qtw87Qrr8aPvwRGhbBH/+vmIfety3d46cI/viL/ynH7YILcjy/L8mnbdtORZqnNQ/0rPt9zpb3IwZun1Dtz56MRxxYjbMaxsTv3BKE5yXMXhclrJxHqf4b1OdRYiRaTf5fvaUeUHr13SuGrwJ3WLVOf5UoP8RfjauDOsQ0tn6HLdahG74jzIuq1pMaBlLvgEPsPQppfnYHGts5YC3dgUBAQKAQwHMLCG/Slp0uIPMdNYtjxxXk278yZ4AThjSnH8ter3pIQjByFSojxrCK4+4MKwz/B+BQDnupCTdfgM72k3Tb8iPMhfpDrLbg3yF+GW6VdZvz36bhza4RXRy89B7MYrNVm3VXW0vRePI/7BW+YFUwdbqqOkmgSwYEuHBHCy6izUf1iWFQjFrHMXbGn3qM6Ran6XhpJst+vtrZ/wcddP3JoLdxz8raChGTcBSOL+tkRpa4GhGT+64+AbgsYAmr88JFCC7874hvXbOXfjkHaXiF56FqI3f11D3TbV0fok/voLxrg8lyP385tb3ebvipgXqM5B/YelJfWyvC7tgOXbnPVRFQCcO60U1R21dlO4VXWUjHXVARdE8jw57xia/gfVWYgGCpdm/LrYVzDv/DGnmqqzlAZKcOuMq61HZv8Es1p1Eb3sLES//Q0NDXWqo+0Xa9UyiFgUx4wZmCP4/WlpbQe8Lu0gAI78xNgJWJZTb3ZVSygSMZ23LOVhI7Ktt7Z8xMdcP5lYMBpzyg8J+12+SwHOcyHaifS7fJedNfL4WGmgREmA0sAgfP/Ab1oPz74TBzcLEb30TES/c3Xal+SdxV9/QZ41odBxUzG6YxaaumNRAAeqzkL9g8UlxcJx6/RPq9uyVOdINZ9LR3GWV59bt1h1lIxkCB3fnvH1oNfwXAWgXXUeogFoqya0u26YdnkwlTdanjUYPzjoGuvh2XfiwEYpohefieh3v6mhsT6VMVIi/uYr2tBcj+7SnTcZY9HWDq9lyzmqc1D/YFlOMcuWxy+t7XTc/X7WlMGo6thid8a6VUfJSOeMPsnMcgXmAfiP6ixEA5VLdz0wIrei8YjSg/r9tsqzhuCHB11rPXjUHTiw3hTRr52G2K3XaGhu6PfbVsWurYFsrLPPnDxYdZSUW7at0x2MWaeqzkH9w3GlTbEit64NrmpJ6cDGgHDEyGz7rS0f8/HWD4YESnDO6JNjAZfvCnD6BdGXifsN78XXTrk47DO8/XIDQ7OH4EcHXWc9eNTtmFEXE9GvnYbo967T0NLUL7c30MRfflacNDzbcVMxVtZ1wefSpoFL8mYklpfUOnx9UzDitB2ufYaGQdk+jVMw+scNUy8L6kK7G8DA3bWAaOD4RBfa85dNODupC91XZJfiRzOvt35z5O2Yti0sohd9BdHvX6+hpTmZNzPgxd9/UxTn+nW3w6ZidEVNtARjMQDTVWeh5GNZTqFI3Dp20dZ2x81XPu2AQajq2CI5BSP5Zg6egpF5w1pcuutXqrMQpQu/y3fjsUMPi43Mrdjv6xqWXYY7Dv6W9esjf4RpW4Mi+tVTEf3hDRpaW5KQNP3I9lbYdbX2KRPVHEip0qKtHR5bct5yJmJZTqG4LY9fsb3Lcff5kaNz7Le2fOysYYYUcGkGrp9ySchveK8CEFedhyiNNBu669s3T78yqKFvT02VOeX48cE3Wg8ceRum1HSJ6AWnIHrbjRrauTRm/P03cdyIPMdNxVi6rdPTHeW85UzEuTWpk+01tMoNTc6ar+zWBUpz/JyC0Q/OHHmC6dE9nwB4U3UWonSjC+1PJb7C60+oPOqA/25+f68HMYbnlOPS8WdbBxSN1bFgnha9/mSgo91xgyBfxpz7gTb03IulgLMOolhZ1wWvSzsQiW6lfE1vSh6W5dSZubUtHDJtmas6SCodP64Y27rrZWesmyPLSVTozcN5Y06Jew3PN1VnIUpTtt/lu/iKiefO/7B2njdofvnO8CNyK3DZ+LOtCYWjdTF/rha97iSgs4PPa7thb1oPmCYOG5GPj6vaVMdJmc6IifZQPF6S7ZkKYKHqPJQ8fDecIpYtj1i6rdOvOkeqHT0m3/54O58zku2rY0+PAHgcQJXqLERpbIWU8qUzR53Q6yjgyNwK3H3Izdb9h38fEzc2abHzT0L0x98R6OxIZc60Y376gXXqhBLHTcVYUtvhAnCY6hyUXCzLKRKMWcevquty3FaYZXlusbBhOUdfkqjQm4c5Q2dJr+H5ueosROnO7/J9/4yRx5s57i8eez0ydxjuOeQ71n2Hfw8T1jVo0XNPQPTOW1iS95L58bvGuGzhuOf+NQ3d3q6IeZTqHJRcnIaRGprXpU1d0+Cs1SCml+dACIiqjq2qo2SUc0efHJVS/hGAMxZuJepf1ba0/3nO6JMv/NOqf7tH51Xi8gnnWGPyhuti7sda9FfXACFOI9tX5tIF8Hm9WkWeD1vav3yKSybZ0BSEpuFg1TkouViWU2NMMGpZHRFnzfc/bdIge3HjSltC8nGWJHmeHBw37Ajp0d0/U52FKFP4Xb7bT66c/dXReZXW6NxhuvjkQy3662+yJO+PaATmmhXWyRPL9Mc/2aI6TcpsaQvDrWvFAHIAdKrOQ8nBaRipMX1DU9BJBwUDAEYUu+y5dYtZlJPo7FEnxmxp/wVAneosRBmkVrPlG+PDHhE7+zhEf/oDgZCzPgnsD+b7b+ozB3kdNW/ZlkBteyQEYIbqLJQ8LMspEDXtmWsauhy1GUnArSPb4zGWNK5SHSVj5LizcHLlbNtneO9WnYUo07jd3uv04sEx4XarjpIxzHkfozjHq2sOG59fWdflk1IeqDoHJQ/LcgpETevwTc0hRz1dHD+uCHXBRrsr7qx1pfvTGSOPj1vSfgYAJ4ETJV8tbOtf7nO+xg1+kkQ21UO2t2F6uaNWTMW6xm53d9Q6WnUOSh6W5f4nvIY+flOzs0rjIZV5cm7dYke9QehPAZcfp4041vK7fHeqzkKUqYQ/8BP36RdY8PpUR8kY1sK59pxRBapjpNT6xiB0TXAaRgZhWe5/w6KWjfawsw7uK83T5YrmdSzLSXL6iONMW9ovgesqE/WnaljmR65jT3bcMSb9xVw8T5ucb9iqc6TS9o4IdE3kAihUnYWSg2W5/02vag45qilneXRkebzaujb2umTw6G6cOep40+/y3a46C1GmE1nZ93rOv8xZHwX2I2v5YuTl+jUnzVuWAGpaQ2EAnLecIViW+5lp2TNW1zvr4L6jRxeiPthkR6yo6igZ4bDSGbBsax6A9aqzEDnAeyI7p0WfdpDqHBlBtrcCHR2YXJqjOkpKrarv8ts8yC9jsCz3s1DcOnJTc8hR9/PBw/LspU2r+DFmkpw6/JiuLHfgIdU5iBxCwuf/uefcS7h2XJKYS+bZc0Y7a0ZCdUvYFYxaM1XnoORwVIlTwa1rB2x02MF95fmGXN68VledIxMMCZRgWE4ZALyiOguRUwhN+6s+eboQg0tVR8kI1pL52hSHzVve0hYGgEmqc1BysCz3r2IA3qbumOocKWNoQI7Xo69u2ag6SkY4ruJwU0r5FwDOeRARqReELf/kPu08/t0lgblqOQqyvY7qG7XtYfhc2lAAHDjKAI568Cowtr4rGlEdIpVmDstHZ6xbdsS6VEdJexoETqw8KuY1PI+rzkLkNMLne9x94mkWNL5M7i9ZVwtN2hhe4Jwl+cJxG6G4FQMwTHUW2n98FuhfY7e0hh213fNhw/Oxonmtoz5u6y9TiydAE9pWACtUZyFyoNUQ2jZ9CpfLTQZz7SrrmDFFqmOk1Na2iAlgguoctP9YlvtRzLQnVreG/KpzpNLIYre1onkdP3ZKgpOHzwn6Dd+DqnMQOZbP/7j7hNPDqmNkAmvxZ9r0wX5LdY5U2tgc9AMYrzoH7T+W5X4UMa1pte0RB60uCeT5DW1jR43qGGkv2xXA9JJJuibEP1RnIXIqoet/Nw6bo8HjVR0l7Vmrl4shPuGo18Oa1rCrO2py+bgMwLLcj3QhRm9rd86U5QKfAZ/hFls6t6mOkvZml8+ScTv+OoB21VmIHKwO8ehiY9YRqnOkPatqA1w+n6Y7qC5vaQtDSkxRnYP2H8ty/zG8Ln3Q9k7nlOXDRxagIdRsmdJRn7T1i1OGz+kOuPyPqs5B5HQiO/cx9yln8Yjl/RXshgwHMX5QtuokKbO1PQyvS6sE4KC3CJmJZbn/DO+KmtG45Zy9OSYMzsa6tmrVMdJeoTcPJf4iHcD7qrMQEV7QJ031wO+ojVj7hV1TbU0td05Z7o5aiJq2DaBcdRbaPyzL/WfstvaIqTpEKpXnu6z1bVU8uG8/TS+ZhJgVew+Aox4/RANUF8LhRcaMg1XnSHvW2pVidFFAdYyUauiKxgCMUJ2D9g/Lcv8ZW90Scs6ikgDyfJqo7tiqOkbaO2TI9O4sd+BZ1TmIKEHk5P7dddickOoc6c7etE6r9MNR8/TqOqM6gErVOWj/sCz3k2DMnLKlPexWnSNVBIAsj1fb3FmrOkpa04SGyUXjXADeUJ2FiD73qjHrCAFnLeaQdNbmTcj1Go7qHds7In5bykrVOWj/OOpBm0qmLUc3dEZVx0iZYfk+hONhBE0uSbo/xuWPgCmtWgB1qrMQ0eeqIWWTPnai6hxpzd5SDcPnE4bmnDcdDV1RLRSzxqnOQfuHZbmf6EKUNQdjqmOkzAFl2dgWbHTO0Yz95KBBk0235vqP6hxEtAuX+xnjkCN5HMH+iMdht7Vg0hDnHCzZ2BWDLeUo1Tlo/7As9xO3oRU5qSyPKgqgprOWZXk/HVI6I+TWXa+ozkFEXyQ8nheNo47jvOX9ZFdvsKeW5aqOkTKN3VEYmjZUdQ7aPyzL/SNbAEZ31DnHMZTmueSWru18PO2HPE8OSnyFLgBzVWchov/xmVYy2IPsHNU50pq1ZqUYU+icHREbu6LwGFoh2LfSGn95/aO8I2I6ZzcSAAUBzd7e3aA6RlqbXjIJUSv2IYC46ixE9D/iMhxeakyapjpHWrOrNogKv7BV50iVmCURMe04gMGqs1DfsSz3j/KWYMwxTwYAkO12aduDjapjpLVDhkwLZnPJOKIBSwSyXtGnHcQ3s/vB3r4VWR7DOUf4AWgJxmIAhqnOQX3Hstw/htZ3Rg3VIVLF0AS8hls0hJpVR0lrEwvHCHDXPqIBS+j6B8b0g7nkz36wGxtgeL2OKsv1nVENLMtpjWW5H9hSDq3vjDpmQ5JxJQF0xrphSefM0U62PE8OvLoHADapzkJEvVqolQ71we1RnSN9hboB20ZprnPuw7rOiBcAD/JLYyzL/SAcs0Y3dUcdc99OGJKNumCjo6adJNvovEpEzOhKAFxRhGjgCiMSqtJHj1edI63Z7a32pMHZqmOkTFso7oqZNucspzHHFLpUsqQc4aRl44YX+FHTtV11jLQ2Jm+47TM876vOQUR7YLje18cfoDpFWpON9daIIr/qGCnTEY4jZtnlqnNQ37Es9wMhxKC2kHOOARmc47IaQk18LO2HiUVjul266zPVOYjoywmff4E+dkJQdY50Zm/fKkqynDMNozNiwpJyiOoc1HcsOP3AECK3M+KcjZ5yfMJujXSojpHWRuQMNQAsVp2DiPZohT56HA/Q2A/2tq16oU93zH3YETEhIIpV56C+Y1nuB4YusrqizinLbkNobSzLfZbtCsCjuzUAW1RnIaI9Wi0GDfFD01XnSFuyqV7k67Zjjs/oCMdhaKJAdQ7qO5bl5HPrQrjCcecc7+bSNa010q46RtoallOGsBXdBB7cR5QOuhGLtWplXNygr+zGevgN4Zj+0REx4dKFc/b4zkCOebCmUH7EtKOqQ6SSR3cJluW+q8wphy50TsEgShPSjK/Sho9SHSNt2Y0NcLtdjllrORizoAvhAuCcidoZhmU5+QpCMcsxczA8uoAuNHTFebxLX43IrYgEXL75qnMQ0d4R/qy5+vBRzvn4MMlkSyM0j0dojqnLQChuRQEUqc5BfcOynHwF3VHTMU+iQ3K9CMZDqmOktZG5FVEAq1XnIKK9IwxjpTZqLEcI+ioeB6IR5HpdqpOkTHfUjAPgQX5pimU5+Qo6HXRwX2muB+2xLs613Q8F3jwdQK3qHES017Zog0odMyjSH2Q4jDyfoTpGynRETAmW5bTFspx8BR3huGMOkx6c7UFbpJNleT9kuwNeAHWqcxDRXtuiFZU4Z1i0H8hwSJZku1XHSJlQzBIAslTnoL5hWU6+gvaw6ZhngFyfC93xbtUx0pbf8EECFoAu1VmIaK/VwR/wwHDOyGiyyWC3LAo453i3nrIcUJ2D+oZlOclsKfM7I84pyzleF7pinLPcVwXeXMSseJvqHES0T0zEoh2ikJ+q95Xs7pQFAecMzkfitg7AOXt8ZxiW5SSLWzInYjpmYyJke3R0xbsddExzcuV7c2FJq0F1DiLaNzIe266VDFYdI23Jrk7k+xwzroRQ3DLAkeW0xbKcZLYt/XHLOVN4Ax4NwXiIZbmPCjx5EMA21TmIaF+JzaKYZbmvZGc7cr3OmcYSTpRljiynKZblJLOk9Mct5xwk7XMJO2xGVMdIWwXePLg0V5XqHES0b4THU60VcNncPuvs0HO8hmNeLCNxWzNtyQP80hTLcpJJCUeNLHtdQobiLMt9VeTLj3sNzxbVOYho3wiPt1lkZTnnyT7JZLAbOS7pmDmLUdOCadnc8jpNsSwnmYT0xhw0suw2BEJmWHWMtFXsK4yAy8YRpaMOkZ0bUx0iXclgNwIOaiAR04Zly2zVOahvHPRQTRmfk6ZhuHSW5f1R7Mu3wbJMlI46RXaOc3agSjIZ7IYB2zHHu0RNG5YEy3KaYllOMgn4nDQNQ9eEiFocXOmrgMsPAFw6jij9dIjsHMdMI0g2GeyGLuCosgxwZDldsSwnmQA8ThpZFgBs6ZyfN9k0oQkkNiUhovTSIbJznDMykmzxOIRwTFdGzLQhAZ/qHNQ3LMtJJ7wxB40sA4AtnfXzJlPPiwXfbRCln04EuLhBn0kbAtIxbbnnVZKdK03xF5dkThtZBgDJkeU+08CyTJSmQsLtcUzZSzrbdtTIsi0lIJ0z7STTsCwnmRDQLdtJI61CcGS570Ti1YLTMIjSj+Wotpdstu2o5tjzMsnOlab4i0syCViaw54/bQ6M9plI/AnyDiRKPzY0voT2mcMGWSTw+RM+pR/n7DWZKhK25qCuLABIhz3pJZOWGJliWSZKP0Jk52Rn/3ee6hxpS4ZD2ktfn6E6RspEYvESwKU6BvUBy3Ly2Y4aWRaCB/jth55PcTkNgyj92LK7q7v74tN4lF8f6GMnwPPjX9r3/vznjhhtHT58OM4888xG1Tmob1iWk0wCluagoWUuHbd/BDiyTJSmdFiWRCyqOkd6sixIKWGaztjXxbZtAHDGD5uBHPGOLsUsB3VlSACa4MOor3rWWWZZJko/Oueg7QeHvW5oifnt/BQxTTnr0ZoS0lEH+Nm2DbfOOVh91bOBFV9widKPGw4ZFe0Xuuao9xqccpfeWJaTz1EH+FlSSpblvotYEQsAt0AlSj/ZMhR0TttLNk0HHDRQ0DOyzHdXaYplOfkcNbIspYRHc6uOkbY6Y0EbQLHqHES0z7JlKKg6Q/rSOLJM6YNlOfkcVZYtCY4s74eOaKcAUKQ6BxHts2wZ7OZraB8Jr89RKylxZDm98Q89ySRg6g66V6WEdOscWe6r1kiHAY4sE6WjPNnZzhWl+kj4/YhblmPaMkeW05uDal1qCKDbY+iqY6SMLQEPR5b7rC3a7gFHlonSUYFsbfGoDpG2fAHETNMxH8NqmgYhBEeW0xTLcpJpQrT7Xc65W6WE9Oh8veir9miXHjGjZapzENG+kZHwYNnZ4Zwn+yQTfj8i8bhjRpa8Xi80TWtWnYP6hn/oSaZrotXndszfP2ImBOcs911nrAtxO16qOgcR7aN4fIjsaledIm0JfwDhWFx1jJTx+XzS5XLVqc5BfcOynGRuXWvxuZxTlkMxqfsNn+oYaasj2gUp5WDVOYho30jbrrSbm1THSFsiO8cOh8OqY6RMIBCIa5rWojoH9Q3LcpLpmugMuHXHzEvqCFuiwJvHgxb6qDPWDSE0zlkmSjPCMMpkU73qGOkrN18Gg85Zei8QCMQAtKrOQX3Dspx8XVkewzGfLbWH4yjw5jnmiOZk64h2wdD0PNU5iGifCHi8xdJB0wiSTeQViK6uLtUxUsbv91tgWU5bXPYm+bqcNLLcEoxi+OAcxxzRnGwdsS64NFcuAB1cVogoXRTJWFTP+sMzgKbBbm+RsqHetmtrYNfW6Hb9dsjGOtj1dZBt/OR9d7TcPNG5vkp1jJTx+/0A0KY6B/UNy3LydQU8uq06RKo0dseQ485mWe6jqBVDyAzHctxZFQCqVechor0ycpvV0XnNW7fn5ntyMTZ/hBiRW6EPPWAIhhw4yS7S/DJg+ITh8WrQDdjtrVI29pTprTW63bAdsqEOdsN2yNYWwEGbc+wgsrJFR0eH6hgp4/V6NXBkOW2xLCdfV8BBB/jVd0aR5Rq0X2W5/rMarP/HYgS3dcBT4EflKRMw4vRJXziPlBKbnl2OmtfXItYVQd6oIkz4+izkjij80ut+9Yw/7fZ0zdBw0rOXAQDMcBzLH/4ITUu3Ias8D1NuPBJZZbmfnzfeHcX71z2Hmbcfj9xRyZ9evK27IZ5TkDUWLMtE6WJUbXe9DgBt0Q58Vr8En9Uv2fG9L0xvzHFnYWz+CDEqd5hePmEISqePt4v0gB3QvZrh9mrCcMHuaJOyqd62a7fC3lqt2fV14v/LdDNgZ974i/AH0N7erjpGyng8HgMsy2mLZTn5Op20dNy2jgj8Lm+fy3LrmgYsuu8dDD1mDMZfNhPt65uw9ukFEEJg+GkTPz/fpueWY8MzSzH+0oOQVZ6LqhdXYd6PX8eRD50Jb76/1+s/9L5T/+e0hT99G/njSj7//43PLEVweyemf3cOat/dgGUPfYTDdrrc+n8uQcmBQ/ulKAPA5s6t3vEFI8cBeL1fboCIksqS9qgtndt7f+LZSWesGwsalmNBw/IdJ2nYqVBnuwIYkz9cjMwdpg8dW4rSqaNlsRYwswxfoky73JAdbdJuarDtbVtgb63RZP02YTf0TPNoaUy/Mi0E4PHCSQf4ud1uDzgNI22xLCdfe5bbOVv4tYdNAAIe3Y2oFdvny2/411IUjB+EydcfDgAonlaGeDCKDf9egmEnjYPm0mHFTGz6z3KMOnsyKk+ZAADIG1uC977xDGpeW4OxF83o9frzx5Z84f/bNzQh1hlB6ZEjPj+tedl2jDp3CoqnlSNneCHevuwfMCNxGF4Xura2Y9v7G3Hkw2ft88+2t2o6t3lC8fBUv4tL8BGlg1A8NGVbsCEpB8h3xYNY1LgSixpX7jhJYKfXZr/hxZiekemK0aUYcsDRcpCeZQYMr+ZyeTXh9kB2dki7ucG2t22R9pbNuqzfnijTDdshmxoBe2AdDiFy84F4DHa6lfw+crvdkFKaAKKqs1DfsCwnX6PfYVvaxay4zHVni8bwvh/I0lndgsqTx3/htKKpZah+aRXa1jWicNIQtK1thBmKY8hhwz8/j+F1oeSgoWhcVPulZXlX2z+qgu41MOigis9Ps00bWs+nAbon8SchzcST+Jo/zcPIsyZ/6ej1/traXQdTWlP67QaIKMnE+LpgY0puKWRGsLRpNZY2rf78xrHTa7fX8GJM3nAxKrdCrxhRitKJs2WJnmVm617N5fZqwu2F7O6E3dRg2du2Srl1s2HXb4PdUA+7fjtkUwNgpfaYdFEyCFY0asMhK3L5fD6Yptmt644ZR8s4LMvJ16ULobl1gZjljIM2oqYpi3z5fSrLdtyCML74fKn1zPnuru1A4aQh6K7tgNAEAkNyvnC+rPI81H2899N8pZSo+2QzBs2s+LwUA0DuyCJsfWs98seWoOb1tfAPyoYry4OGhVsR3N6JGT88dp9/rn1R07kNXt0zBokXQWc8aIjSl9tneEZUd2xVnQMAEDEjWN68Bsub1+w46YtlWnNjZH4lRuUN04dVlqF0whFykJ5tZetezefyasLjhQx2wW5utOW2rdLeslmzd0zzaOgp0/HkLpGnFQ+GaZqOea7z+XywbbtTdQ7qO5bl5JMxy+7M9bnym7r3fVpCOgrFbbvEX6Stbt24z5f1D85Bx4bmL5zWsSGxK1a8K/GJVTwYhe51QehfLNWuLDesqAk7bn1esL9M6+oGRFqCKD18xBdOH33+VMz78et465K/Q/camPG9Y2CbNtY8OR/jLz8Iej8fsNke7UTMjgu37hoKYEu/3hgR7a8pTeHWcMSKulQH2RsRO4ZVLeuxqmX9jpO+UKbdmhsjcyswOn+YVjG0DKVjD5WDjWwzR/cJr8ujaR6fkKFu2M1Ntty+1ba3bNbtum3CbqyDrN8Ou7EeiO/ba51WPAjd0aijyrKUkgf3pTGW5X5gWrI11+ucstwVhjbIXySReBLeJ8NOHIeVj3+KLW+uw5BDK9G+oQlVLybm7gl9p6vb3TXveKoVe3ez2z+qgivLjeJpZV843T8oG0c9ejZC9V3wFQWgewxUvbgS3gI/Bs+qROuqeqz83VxE28IYfGglJl558F6V832xubM2NqlwzBSwLBMNdLNWtaxPi6K8N2J2DGvaNmJN2+eDHV8o04ZmJMp0bqVWUVqmlY0+WA42cqwc3Qu34dU0r0/IcAh2S6Mtt2+z7S3Vul1XK+yGup7l8eqA2Ben6orBpXZrV7dj+ofP5wO4EkZac8yDNZUkZGO+3xipOkeqNHbHtfKsISb68HgaesxodG5uxcrHP8WK334C3WNg3CUHYtXvP4M7N3HAmyvggRWOQ1r2F0aX48EYdI8BzdjztDfbslE/dzMGH1K526Kr6drny8XFOiPY+OwyzLr7JFhxC4t/+R4mXHkwiqeWYf5P3sCWN9d9fqBhsqxr3RQYXzBqmi60l5N6xUSUVN2x4NGrWjY45mhc0zaxrq0K69o+30BEILGJEgDAgIbK3AqMzq/UKgeXaaUjD5RD9KOtHN0Hr8uTKNORCGRLk23X1dr2ls2aMf1g0drQvNvby0Q+nw+apqVmkjv1C5blfqAJUZPvdx+iOkeqbG+PYNqIQX26rNA1TPrGIRh74XSEm4PwD8pG97bEQvX5Y4sBAFnluZC2RLC+6wvrH3fXtiOw0/9/mZbl2xHriKD0iBF7PO+6vy7CkMOGI6eyAJ3VLbBN+/OpG2WzR6FlRV3Sy/Kmji1GKB46ItudldTrJaLk0oQ2a6fi6HgmbGzs2IyNHZt3nPSFMq1BQ2VuOUbnVWqVxWVaWeVUOSm3VDSvWqsirhI+nw+GYdSpzkF9x7LcD7wuvbrA7+rTtIR0VN0axgkTi/frqGZXlgeurMQiIjX/XYP8cSXIKs8DAOSPK4Hhd6Huk2qMPm8qAMCKmmhcuBUVx4/dq+vf/mEVPPk+FE4a8qXn69zcirq5mzH70bM/P02a9uej2lbU7Jcj8Na2boJbd88ED/IjGsiKdE0vqO1i79lbNmxUdWxBVcfnM8zEv05+BFVVznnD4ff7TcMwmlTnoL5jWe4Hhia2lWR5IgAc8VHdmvou5LiHaxoE7H3seW3rGtG6pgG5wwsQD8Wx/aMqNC/ZhkN+fsrn59HdBkaeNRkb/r0Uriw3ssryUPXSSkhbfmGEt/a9DVj+8MeY/fi58Jf8/witFbdQP28Lyo8eBaF9+fuXVX/4DKPPnQp3jhcAECjLhe4xsOapBSicXIrNr63BiDMmfel19EVjuAXBeEjz6O5JAFYk/QaIKBlmbu6sDduQbtVB0pVHd8Oju9HW5pz9ObKysqLgnOW0xrLcP+pKst1xOKQst4TiiNsm8r15aIns2xOgpmuo+7gaG/65BEII5E8YhEN+fgpyKgu+cL6RZ09ObHn93HLEuqLIHVmEg39yIjx5/38XSxuQtgTkFwt706JamKHYHqdg1M3djGhbGMN2WvdZdxuY+p3ZWPnEp9j69gYMObQSw04Yt08/495a1LDSOLbisGOEECzLRANQ3DYPWd60JqA6Rzob7C9GOB6WUkpHfPIKAIWFhSaAvV/nlAYcluX+sb0o4HbUR+mheNwe7C/S9rUs544qwuG/PG2P5xNCYPS5UzH63Km9nmfoMaMx9JjR/3P64FnDcMoLV+zxNoYcUokhh1T+z+lFBwzB7EfO/t8LJNnCxhXeWUOmnpntzvpNv98YEe2zsBk5dk3bJr5u7ofBgWKEwxHHTFMEgPz8fAPAvq+tSgOGI3bPUaC6MOB21C5+7SELZVmDVcdIayua18Kre2YCyJhlqYgyiObVPVPWt3GAcH9UZJci2NHlmO5hGAa8Xq8HwMDYxYb6xDEP2BRrdOtC87mcc/duaze14blDbdU50llnrBuN4ZYYgJmqsxDR/ziwI9ZltUe5Edv+GJM33GpqcM6xbvn5+YjFYk0ALNVZqO+c0+ZSS0ZMu35wtnMGl9c1dGNk7jDVMdLe/PrlPtM2j1Odg4i+KGbFz/6wdr5zntT7SWVuudi61TmDrAUFBbBte5PqHLR/WJb7iS1l1eCeFRWcYFFtJ8qzBvHxtJ+WNK50hc3IGapzENEXxe34Vz+tW8QpUvtBExqKfQVadbVzprIUFBTAMIyVqnPQ/mG56SduXVs9JMc5gxC17WH4XT74DOe8QegPq1o3wGt4JwDg7iREA8doCRRxvvL+GRIoRsyKo7u7W3WUlCkqKoq43e7VqnPQ/mFZ7idel762LM8bUZ0jVWwJdEQj9rDsMtVR0lrUimFzx9YwgCNUZyGiBFvaZ8zdvlhI7he0XyqyyxCNRR01d7e4uDgKroSR9liW+0/V0DxfVHWIVGruNsXw3HLVMdLep3WLs8Jm5FzVOYgoIRgPX/zx9gX82Gw/VeaUy+62LscsGQcABQUFOliW0x7Lcv+pGpzjcdT9u6UlLkbnDeeKGPvp/dp5mia08wBwlzAi9UrcumvMsua1qnOkvTF5lfa22m2OeV3UdR0+n88HoEZ1Fto/jnnQKrA5z+fy72F35Yyyoq4TY/NHOOgn7h9N4RZs6dpuAThZdRYiwleWNq2Om7apOkfaG55boW/YsEF1jJTJzc2FaZqtAGKqs9D+YVnuP+G4ZXcVBpwzODivph2lWYOELnTVUdLef6vfz+mOBa9WnYPI6bpiwYs/rJ3PA273U8DlR647G05bCcOyLOf8wBmMZbkfxSxZXZ7nnGlu3VEL4XhMVuZw3vL++mT7Qrh19xwA+aqzEDlYlld3z1rYsFx1jrQ3Jm84uiPdtm07Z6ZeQUEBdF1fpToH7T+W5X7k0sX8ygK/6hgp1RmxrHEFI1THSHtBM4ylTatNADzQj0ids9a2VcWCZlh1jrQ3Nn+EbG9uc9Q0vaKiopjH4+EayxmAZbkf+Vz6olFFgZDqHKlU0xLXDygcF1edIxO8UfNhoDsWvEZ1DiKn6o6Hbn1+0xvZqnNkginF42VNdY2jynJxcXEYXAkjI7As96+Vo4r9jiqO82raxfiCkZy0nASLGlZA1/RxACpVZyFyoGmWbQ1fWM8pGMkwKm+YtnKlswZZCwsLNbAsZwSW5f61clC2x1ErYny0qQU57mwtyxVQHSXtmdLCB7XzYdrmxaqzEDlNKB6++eWqdzw2NyLZb4P9xYAEmpubVUdJGSEEAoGAHwAP8MsALMv9qytuyfbB2c7Z9jpmSXREI9aY/OGqo2SEt7d87I1Z8asBOOgtF5FyuYamn/N6zQf8lCwJxuaPQHew21E79+Xn58OyrA4AnPCeAViW+5lp22sqC511kF9tW1wflz+SwzFJsLZtE4LxcC6Ao1RnIXIKW9qXLG5cZbdHO1VHyQgTCkfZzXVNjnrjUVpaCtM0F6vOQcnBstzPfC79s8oCv3PWygEwf3MnZgyapDpGxvjn+pcD3fHQXapzEDmECJvRW17c9BbnkiXJ9OJJWL9+veoYKTV06NCY1+t9R3UOSg6W5X7m0rWlo4sDQdU5Uunt9U0YkTNUuDWX6igZ4d2tnwpIeSCAKaqzEDnAkcF4sGBFyzrVOTJCjjsLhb58xx3cN2zYsLCmafNU56DkYFnufytHOGwaRjBmozMatccXjFIdJSPEbRPPbvyvOxgP/1h1FqJM1x0P3fL8xjc5qpwkk4vGIRgOWvG4cxaG0jQNxcXFfgCLVGeh5GBZ7n/r8nyG12s4666uao5p00omct5ykrxW/b6uC+0kcBk5ov402KUZx76z9VMeUJskBw2abDXU1jvqBbCkpATxeLweACe9ZwhHPYAViYXj9sZRxc4aqPhoUzsOHjxVdYyMETLDeLX6PS1sRn6oOgtRpoqY0e+9t3UuQtyxL2mml0zSlixZ4qg3H2VlZZBSzlWdg5KHZTkFXLr4YGxJwFGjrB9sbMFgf5EIuJw1BaU/vbDpTbcmtK8BKFadhSgDFQshrv7nupe9qoNkikH+IvgMr1i3zlnzvysqKkI+n+991TkoeViWU8Dr0j+cXJrTrTpHKpm2RGs4Yk8uGqs6SsZoi3bgw9r5ImbFbladhSjTRMzoD9/bOldrjrSpjpIxphSNR0dHh6NWgwKAiooKE8B81TkoeViWU2Pe2EFZjlpjEgDWNcS0A0smO+6Jsj89s+FVrwS+BSBLdRaiDFIihLj6H+teds4OUikwc/AUu2bTZkf1DLfbjZycHB8A7pOeQRz1IFao2q1rVmHAWUupvbe+BQcOmszHWBJtDzZiadNqYdnWN1RnIcoUETN627tbPxUtHFVOGg0Ck4vGaQsWLFAdJaWGDBmCaDS6AYBzlv9wABaZ1JBR014yrsRZg4ELtrTD7/KhLGuQ6igZ5a9rng/EbfPHAHJVZyHKAEMBXPUPzlVOqjH5I2BZlmxqalIdJaXKysqkYRgfqM5BycWynCIBt/72+MFZjnqnKQE0dsXMQwZPd9TBjf2turMWn9YtckXM6J2qsxClu1A8fN/LVe8YrZF21VEyymGlM+yGbfWOWgUDACorK7vdbvfHqnNQcrEsp4iuibmTS3NCqnOk2rzqTmN2+SzOW06yP69+zgfganDdZaL9MQnAGc9seM1Zc+RS4MiymWLJ4iWqY6RceXm5Bh7cl3FYllNnQUW+z69rznqj/Z/l9SjNGqTnezhjIJlaI+14fuMbRige/o3qLETpKhgPPfj3dS95uK5ycpVnDYHf5ROrVq1SHSWlsrKy4Ha7BYBNqrNQcrEsp05HzLTrhxf4VOdIqa6ohZZQxJo5eIrqKBnn2Y3/dZnSOg7AoaqzEKWhY+K2OevV6nf5OphkhwyZJjta203VOVKtrKwMsVhsGRKzECmD8EkihTRNvDm5NMdxf0Rzq7r1OeWHWKpzZJqoFcMTy//uC8XDTwJw3NKERPvBFzYjf3lwyZP+uO24TtfvZpfPsteuXmuozpFq5eXllsfjeVd1Dko+luUU8rn0/84cltelOkeqPbN0O8bkD9d9Bg82T7YPts0TW7vryizb+qbqLETpImJGf7q0aXXuggYuhZtsBd48DAmU6J999pnqKCk3bNiwbl3Xuc11BmJZTq33x5ZkeZ02b7kzYqElFLGnl0xSHSUjPbTkzwHTNu8Ft8Em2hvTbGl/89Flf/GrDpKJZg6agvbOdisSiaiOklKGYaC0tNQHgGU5A7Esp1ZLzLK3jikOqM6RcvM3B7Vjhx7muCkoqVDTtQ1v1HzoCpmRB1VnIRrgjFA8/PffrfyHtz3aqTpLRjp+2BH2muWrHTctrKKiAvF4fAOAVtVZKPlYllPMpYtXp5blOG7+7j8Xb8eU4vEiYDjrAMdU+cvaFzxxK346gBNUZyEaqGJW/Oaarm1D397yibM+3kuRIm8+huWUaR9/7LxlhseMGRNzu93PqM5B/YNlOcU8hv7GzGF5QdU5Uq0zYqKxO2wfWjpDdZSMFDYjuHfhY/6IGf0HgCLVeYgGoOG2tO98YPEfnffRXorMLp8lm5ua7XjcUftvAQDGjx8f1XX9VdU5qH+wLKfeR8ML/T6P4by7/p11Hdopw+dwKkY/WdG8Dq/XfBAIxcN/A8CRM6L/J4Lx8F/+uf5ld12wUXWWjHVi5VFYNH+h417c8vLy4Pf7AWCx6izUPxz3oB4AuiJxe934QVmqc6Tc88vqMTS7VBT7ClVHyVh/Xv2cuy3acZgl7StVZyEaKGxpf6010j71PxvfcNxyZqkyPKccue5sLFnivF37Ro0aBcuy3gLA3WozFMuyAl6X9tLUshzHLe4ZtyW2toXtOeWzOLrcT0zbxE/nPxqIW/EHAYxSnYdoABgas+KP/nLR7wO2ZJfpL8dWHG5vr90OKZ339D5+/Pgur9f7nOoc1H9YlhVw6dpbB1fmh1TnUOHllS3aiZVHqY6R0bZ0bcfTa/7jCcXDzwNwqc5DpJAnFA+/9s/1L/s2ddSozpKxNAgcPfRQ7eMPP3Lc9C9d11FRUeEB8KbqLNR/WJbV+HRwjsed53PeJ4Jvr2tGliuAkbkVqqNktJer3tE3dWwZHrVid6rOQqRK2Iw8srp1w4hnN/zXeU+2KXRA0ThIy7arq6tVR0m5iooKmKa5CUCz6izUf1iW1YjFTPvdgyryVOdQYmNTBMcPO9Jxy+elkoTELxY+ETBt82YAh6jOQ5RqtrQvDMbDF/5i4e+4+Ug/O6lytl2zcbMj+8SYMWPibrf7WdU5qH858sE9EGR5jH8cObLQcVtfA8A/F9WJY4Yeqnt0t+ooGa0t2oHfLPmTL2xG/gMgT3UeohSaGLPiv7/zs9/4Q2ZYdZaMluPOwkGDJ2vvvPOO6ihKjB8/PqLr+iuqc1D/YllW57+ThmR73Lrjpnhh2fYuBGOmdWTZTNVRMt7cuiV4d+vc/FA8/Ao4f5mcISdsRl7/7fK/+jZ31qrOkvGOqzhcdnV2me3t7aqjpFxubi4CgYAAsFB1FupfLMvqtERNe/WUshzVOZR4e227fvaok3hoego8seLvng3tm6eFzcgT4PrLlNlEKB7++8fbFha9u/VTPtb7mYDAGSOPx/y58xw5J/z/2rvv8KiqxH3g77l35t4p6RWSUAKBUEJHBakKWBEQFVGxL7iirrrW9bvqupZdde3Yey8oIuLa6FZ6k957SyFl+r33/P6YsD+WJUhJcieZ9/M8eTDJnZl3YjLzzplzzykoKIBhGNPAJeMaPZZlG3l19YNT89OCduewwwcLdyLNlSLapuTbHaXRs6SFh+ZN8OwPVYwKm5E/252HqK6EzchtxYHSgS8se9dld5Z40C2rI5zCgXnz5tkdxRbt27evdLvdXDIuDrAs20gR4oveLVPj8hWpYUms2h2Qw1sP4Yl+9SBgBHHPT//yhs3wgwCG2p2HqA70Nyzj73/79RlvxIq7ZextMbL1mea6lWvjcgRfVVW0aNFCB/Ct3Vmo7rEs22utqoj9BRnxebL2Kz9tVXo17aYmOL12R4kL+wIluP/Xp91BI/QRgC525yGqRZ2DRmjqI/NfcO8NlNidJS5kutPQPr1A/e67+FxeuFmzZjAMYxOAfXZnobrHsmwzVcHEU1qmxuXo6vbyEIqrQuaQ5n3ib8snm6wp24hnFr/pCRqhaQCa2p2HqBa0DhqhWc8sfjNhyb6VdmeJG+e0PM3avWu3FQjE52ojbdu2jTidTi4ZFydYlm2mO9RJAwrSfXbnsMvExfvUEa3PhOB5Z/Xmh53zxWfrv0n2RwLTAMTn2xrUWOQEjOCPr6/4OPmHnfP5IFJPHIoD5+QPVKZ9+33cdoiioqKQw+H4wu4cVD/i9hc9hvyU7nHKnCTd7hy2+H5NMRyKJns17Wp3lLjy4ZopzgV7l+f7I4GJAFS78xAdh9SAEfzh03Vfp3+9eTafy+rR6c16y4A/YG3bts3uKLbIzc2FruuV4JJxcYMPMPYzJfDhwDbpcTkVAwC+X1WujGl3flye6GinJxe97t5csWNgwAh+DCAul36iBsvrjwRmfr/lh9yP107l+uH1SIHApYXDxY8zf4jb/tC1a9eQqqqvAuAUwjgRt7/sscTtVN8+o11mfE78AvDu/G3IcKeLTumFdkeJK4Zl4N5fnvBsLN96tj8S+BQszNQwaP5I4Ou5u5cUvvLbR/H5lpyNejXtDg0Oa8mSJXZHsYWiKOjcubN0OBxv252F6g/LcmyYm6A7Aq3S43P6qGEBczdV4rJ2I+J2dN0uITOMe39+0rOhfOsQfyQwCdzlj2Kb6o8EJq4sXdfjqcVvcC1lG4xpN8JaMn9x3HaHgoICWJa1HsBGu7NQ/YnbX/gYIxUh3j6tTXrE7iB2efHHLaIgpYWan5Rnd5S4E7YiuO+XJz3r9m8e5I8EJoOFmWKTEjCCb2yp3DHo4XnPeyzJmVv1rVNGITJcqWLWrFl2R7FNt27dfC6X63m7c1D9YlmOEbpDeXdQ24xwvJ7O7Y9YWLnbb11SOIyjyzaIWAbu//Vpz5qyjQP9kcCXADS7MxEdxOmPBD7dWrHzwvt+eYqbjthkTLsR5rpVa2FZ8flCRdd1FBQUOIQQE+3OQvWLZTl2LHeooqRddoLdOWwzYc4WpUd2JzXbk2F3lLhkWAYemPusZ2Xp+v7+SGAqAM4HpVjg8UcC364u23DmX356zBMwgnbniUv5Sc3QOrmF+vXXX8frmA7at28PwzB+AMCdb+IMy3LskJqqvHF624yQ3UHssqcyjK2lQWNUm3M5umwTwzLw0Nzn3CtK1vX1RwJfA+C8ULJTsj8SmLNg7/JeD/z6rCdsxe1MNdtd2m6YuXXTVisYjN8XKz179qx0u90v2Z2D6h/Lcgxxqsr7AwrSLCVuX7cDT8/a4hiY10vNdKfZHSVuGdLEQ/MmuJcVr+7ljwRmAEixOxPFpZyAEZw/e8fcoscXvOI2JV9D26V5Yg66ZXZUv5g8OW47Q1JSErKzs1UAX9mdhepf3P7ix6j1UmJL19xku3PYZnNpAOv2+a0r2o/kM6ONTGnikfkvuGds/6V7wAguAdDK7kwUV4qCRmjpJ2u/yn9+6bu65HK2trqqw4XWxnUbpM8Xt5vNonPnzpZlWZ8BiN+h9TjGshxjvJr63Lkds+L3EQnAv2ZsVk5t2kPN8WbbHSWuWdLCS8ve199eOSkvaIQWAehjdyaKC6cHjdAvzy15O33iun9z7W+b5SfloXNGO2XKF1Pi+D1PoEePHn5d11+1OwfZg2U5xggh3u+el6wmueL3OWJfVRhLdlTKqzteGJ+nXMeYqZumq/+Y/0JywAh+Z1rmGLvzUONlWubVfiM49YG5zyTM3jE3rstZrLi64yhr9YpViOe5yk2aNIHH4wkC+MnuLGQPluXYUx6xrCmD22bEdVF8csZm0S2zo8J1l2PDwr2/4fY5j3jKw5UvB4zgc+Buf1S73AEj+F5psHzC7XMedi8vXmN3HgJQmNoK7VNbK1O/nGp3FFt16dIlrCjK6wDi+nk5nrEsxyCv5nhuWKdsv9057FQVNvHjhgpcW3QxH5xixJbKHRg/4z7P+v1brvFHAnMAcI0/qg2FASO4fPHeFSPHz7zXs7Vyp915qNp1nS6VSxctgWHE77rWQgh07drVdDqdb9mdhezDshybfvJoalmHJvG75jIAPP/DZrRNaSUKU3luWayoivjwfz897vl2y5weQSO0AkA3uzNRw2VJ65KgEVr4xopP8h+Z/4KbayjHjm6ZHZHrycZ3335ndxRb5efnA8BWAKttjkI2YlmOTdLlUJ8Z2jG+R5fDpsQ3K8vEuKJLeCp8DLEg8fqKT7RnFr+ZGTCCP0Us43bwsYSOjStgBN8oDZa/dueP//R+vXk2f39iiIDAdZ0ulfN+nividbe+A7p16+bXdZ1rK8c5PkDFKFUR7/Rqmap6NdXuKLZ6c+42NPU2xcnZXeyOQof4Yed8cePM+92byrf9zR8JzAOQb3cmahAK/JHA0qX7Vl18/Yy/ejaWb7U7Dx1icPM+Mkn1ytmzZ9sdxVZutxvt2rVTFEX5wO4sZC+W5di1L2Ja0wa2SY/rUVVLAu/P3yPGd7kcDhHfLxxi0R5/MW6f87D3o7VTuwaN0HLDMscC4CoGdFiWtC4MGqEl76yaVPDQvAncujoGuR0uXNvxYvH919/FfT/o2bOnaZrmFwD22p2F7BX3fwyxLEF3PD2iU5Mqu3PYbeqKvTBNpzW01aC4fuEQqyxITFr/jXrbnIe9u3x7n/JFAtMBNLU7F8WUbF8kMKksVPH23T896p26aQafe2LUZe2Gm0FfwFi+fLndUWylqip69+4dcrlc/7A7C9mPD1ixbUaqxxkozPLancN2z87aplzWbrhI0uL7pMdYtqVyB26aeb936qbpfYNGaI0lrVF2ZyLbKYZlXhc0Quu/2Tz73HHT/uJZv3+L3ZmoBjneLJzdYqA6+bPP435pyA4dOkAI8RuApXZnIfuxLMc2y6mKf43s0jSuT/QDgCU7KrG9LGxe03EUt8GOYYY08e6qz533/PR4YnGg7E1/JDAZQJrducgWnXyRwKItFTueuG3Owwlvrpyohcyw3ZnoCMZ3vtzcvnWbuX37druj2K5///5Vbrf7QbtzUGxgWY5xDkV59aTmKSLN47Q7iu3++f0mtW9OT7V1cgu7o9DvWLt/E66f8VfPjG0/nxU0QptMy7wB3MgkXniCRuhJvxGc+9bKiZ1umf1375bKHXZnot/RPbMjClNbKZ98/EncnxzSvHlzJCUlVQD4t91ZKDawLMe+/aYlPxjaMTt+V4WvtrsyhF82Vsqbu11tCZ5DFvNCZhgvLf9Av+OHR5LWlG181B8JbABwLngCYGN2dsAIblywZ/kfx037i/vrzbMVCZ5qEOtUoeLGrlfIeT/PFaFQyO44tuvXr5/P6XQ+Au7YR9VYlhsAj6b+a2hRVsSpsmM8OXOjSHdlYFDzU/kM3EBsqtiOO3/8p/exha803+sv+dgX8f8EoLPduahWtfRF/F+WBMo+fWTeC9n/XPCie3+owu5MdJRGtB5iOS2HNXPmTLuj2C41NRUtW7aUiqK8ZXcWih0syw3DaimxcGBBut05bGcBeGbmNmVc0SUiRU+yOw4dgwV7lmHstL9431n1+Sn+SOBXfyTwHrhqRkPX3B8JvB00Qqu+3Dj9zLHT/uJZvG+F3ZnoGGR7MnBJ4TBl8sTP4376BQD07t07JKV8BYDP7iwUO4SUHKBrIIbsrghOuvbDZVwOAsAjQ9tYAWUzHpo3gS/4GiCvw43RheeFz8k/zRAQ/9JU56MA4v5E1gYkL2AEHxAQl07dNEP9bN3XzsoIu0VD9Fjfuy1HqcQH738Q94+luq7jtttuC2qa1gYAz3Kk/4j7P44GZFqiy7G3R7Nku3PEhAe/3aB0Sm8vuLNfw+QzAnh9xSfa+Bn3ehbt/e32oBHabljmXQD4Cx7bcgJG8JWgEVr79ebZY675/k7XWys/ZVFuoAY1O1W2SMgVH3/0MbsAgB49eliWZX0LFmU6BEeWG5YrV+yqnHDnlFUcXQZwZrtMXNkrA3+Ydje4E1jD1jq5OUa1PdffM7szpJRvuhz64wC4IG/saBowgvcpQrnqm82zlU/WfqWVhyvtzkQnIFlLxKuD/4Fvvvway5YtszuO7RRFwW233eb3er0DAcy3Ow/FFpblhkULRsw9t01embK5NGB3lpjw9MhCa4NvKSYsfZsjI41AhisVIwrOCJ/VYoBlSvN7r9PzIPjEZae2QSN0qxDiqm+3zBGfrP1K54l7jcM9J403myMbr73yGucqI7oJybBhw5a6XK6udmeh2MOC0bCEHYp47NIeuZzbWe1vX29QBuSdorRPK7A7CtWC4mAZXvvtY+3yb//s+mjNl+eWhypn+SL+RQCGg49X9cUBYERV2PezLxJY8tWmGdf+4fu7Xa8s/5BFuZHomdUJ3TI7Kh+89wGLcrX+/ftXulwubkJCh8WR5YYnMWSYO2/6dEXCjnJOPQCAi7s1xVlFboyb/n8wrLhfjrpRUYSCvjk9MbrwvMoMd1qlW9X/KYT4EECx3dkaoeYRy7jGsIwbd/n2aZPWf534486F/JtqZNwOF14d/A+58Mf54ocffrA7TkzIzc3FlVdeuVfTtBwA3CWW/gfLcgMUNq37f9lUdudj0zd47M4SK166uL21uHQuXln+IUcfG6mO6W0xvNVgX4/sTo6IGfkpQfO+CGAqAL5qPH4uREeR/+RQHN1m75iLrzbNdG0s32p3Lqojd/QYZ7Zz5ePFCS9wVLnaxRdf7C8sLLxXUZQn7c5CsYlluWFKDhnWjvETl3t3V3C3JQDI8Drx0ugOeGjuc1havMruOFSH3A4XTm3aA2e3HFDZKrm5aljG5x6n+10AMwBE7M7XALgBnB4wgqMUoVywYf9Wa+qm6Ym/7lqMsMUfX2PWp2kP3Nztavn80xOEz8cVTAAgJSUFN9xwQ8DpdDYBwHlGdFgsyw1UyLAe/nFj6a1PztzotjtLrLiwSxNc0C0N46bfgyouZRUXMlyp6J93sjy92alVOd4sxbDMLz1O9zsApgMI250vhuQCGFoZrrrEpeq9NldsD/2wc0Hijzvmi72BEruzUT1Id6XgxdMf4uoXh7jgggsC7dq1e8rpdP6f3VkodrEsN1xpIcPadt3Hyzz7qtgJDphwUXtzb2iDfHDecw67s1D9ynSn4dScHnJQs1MrmyU01QJmaGGC0/OlIpRZABYCiKfJtwqAHhEzMiJkhi92KI7chXt/M3/aucC7cO9v8EV4jnA8ERB4tO9d3HzkEJmZmRg3blyl0+nMA0eV6QhYlhuwkGE+PmtdyY3PztnssjtLrEhxO/DqJR3ky8vfx/RtPwu785A9krQEdExvi66ZHULdszqGMlypWsAMLaouzzMBLELjmrKhA+gEoKcv4j/NIdSzysNV4qedC1y/7lrsXFW2AZa07M5INhnRaogc1fpc+fS/nlIsi78HB1x22WW+Vq1aPaCq6uN2Z6HYxrLcsGWGDGvL2A+Xukv8jel5/8ScVpCGGwbk4caZ92OPn4sm0IHy3AZdMjqEe2QXBTNcaXrADC5KcHqmKkJZAmA1opugNIQz4Z0AOgDo6Y8E+prSOtXt0POLA2WBVaXrHStL13sW712B3f59duekGNAiMRdP9P8/vPvmO9ixY4fdcWJG9QoYpZqm5QHgxgV0RCzLDVwwYj47bW3xuBd/3KLbnSWW3DOklWya5sctcx4UHFGjQyU6veiY3hadM9qFC1JaBPISmjg8To8WNII7LMhVXod7gaqoqxAt0WsB1PckeAEgE0AegGYAmgWMYJFhGf3cDlebslBFcHXpBmVl6TrvurLN2FSxDSGT07HovzkVB1447UG5afl68d2339kdJ6Zce+21Vbm5ubcrivKy3Vko9rEsN3xNQoa1kaPL/00AeOOyjtacXXPw1spPOUePfpdL1ZGbkI28hKZoltjUapXc3NciMVemu1M9EStSHjGNrYAsVYSyz6k69+iqtg9AWQ0fEUTnDSuI/joqiI4IuwF4DvpIAJBrWEbzgBFqKyHzHUJtqqlaesSKGGXB8tC+QCl2+/bp26t2udbt34wN5Vu5vTsdlZu7Xm11TmiDF597kY+BB2nVqhUuvvjiXbqut0Djmo5FdYRluREIRswnftpUdj1XxvhvWQkanh/VHv9a+DLm7VlqdxxqoBQIZHkykO3JQILTgwTNiwSnB4ma10zSEsPJeqKRpCVYCU4vvE6P6nG4HEIIRUYfXKWEhJRSWtKSYStihs2IDJlhGTLDCBhB7A2UaLt9+1z7AqWiJFCGfYFSlATLOFJMJ+T0Zr3lH4suw4SnnxN+P0/oPNj48eOrsrKyxgL4yO4s1DCwLDcOKSHD3HbzZysStu3niNPB+rdOw00D8nDTLM5fJqL40DIpD//qdw8+nzgJa9assTtOTGnfvj2GDx++3uVyFQLgHD06KnxrpnHYrwjx97GntuDiwoeYs6EUP6wvlw/0ulVqitPuOEREdcrjcOOBXrfIlctWSBbl/yaEwJlnnulzuVw3g0WZjgHLciPhVJXnOjRJCHRokmB3lJjz7JwtwjQ88qauV/LBkYgatbt6XmeaAcOcMmUKl848ROfOnaXL5VoL4Gu7s1DDwrLceAR1h3Lb9X1aVNkdJBbdMXmd0jOrqzijeT/OOyKiRunCNmdbhcmtxOsvv8ZNmQ6hqiqGDBkScLlcfwLA5wE6JizLjYgixPtNklx7erdMtTtKzPGFTTz0zSYxrtMlonVyc7vjEBHVqqL0thjd9jzlg3feV0KhkN1xYk6PHj0sh8MxH8CPdmehhodluXExPZp647g+zX0K34D7H7/trsKni/figd63IkVPsjsOEVGtyPZk4N5TbsL0b6dh586ddseJOZqm4fTTTw+5XK5b7M5CDRPLcuPzrVdTV5zRLpNvMx3Gh4t2YeM+w3ro1NssnvBHRA2d1+HGI33uwOrlq+T8+fPtjhOTevXqZQD4HsASm6NQA8Wy3PhIr+a48aqTmwVcDv7vPZz7vlqvuEWqvKvnHw0BDsETUcOkChX39brZCpb6rS+nfMkHs8Nwu93o27ev4XK5brc7CzVcbFON03xVEV9d2jOXuxochgTw58/XqB3SCpUr2o807c5DRHQ8buhyudVUy8Dbb7zF5/IaDBgwICyl/ATAOruzUMPFP7BGyqOpfzq3Q1YkN9lld5SYtD9g4J4v1ytD8wcppzc7lVNWiKhBGd5qiNWnSQ/x+kuvKZbFVTEPJzMzEz169Ijoun6n3VmoYWNZbrx2q4q4/0/9W3KjkhpsLAngyRlbxfjOY0THtDZ2xyEiOio9szvj8vbnKx+8877w+fgQX5Phw4f7FEW5G8Aeu7NQw8ay3Ig5VeXZVhneYi4lV7OfN5fhgwV7cF+vm9HUm2V3HCKiI8pPysNdPa/DN1O/xo4dO+yOE7M6deokMzMzd6iq+qLdWajhY1lu3CIeTb3mxn4t/TpP9qvRZ0t348f1FfLRvnchzZVidxwiosNq4snEI33uxJIFi+WSJUvsjhOzdF3HOeecE9R1/UoAPC+FThgbVOM3w+kQM0Z1axqxO0gse3bOFrGp2LIe7XuXTHB67Y5DRPRf0lwpeKzf3XLLuk3Wt998y5UvjmDQoEEhRVE+A/Cr3VmocWBZjgNezTH+/M5NjCaJut1RYtp9X61XfAGX9Uif2y1d1eyOQ0QEAEhwevFo37tk6Y5i69OJn/J5+wiys7PRtWvXsK7rt9qdhRoP/tHFh20C4pHx/Xiy3++55bPVqo40+UCvWy2HUO2OQ0RxTlc1PHLq7TKyP2S99+57fFA6AiEEhg8f7nM4HHcAKK7D27lKCDFXCOETQlQIIWYLIYYd5rhxQogRh/n6ZiHEv+oqn12EEHcKIQYe52Vj+mfFshwnNIfyeIcmCftPap5id5SYJgHcMHGVmu3KlXefNN5UuGkJEdnEIVQ80OsWyx3WrDdefZ1F+Xd07dpVpqenb1EU5dW6ug0hxIsAXgMwF8AIABcD2AzgCyHEXYccPq76mHhxJ4CBx3nZmn5W5wN49jivs9awLMePkNupXnnrwHy/28n/7UcSMSXGT1ylFqa0FX/qdhVPDiGieqdA4O6TrreaODLw6kuvsCj/joSEBJx11llBXdcvBVAnC09Xj3z+EcCNUso/SSm/l1J+LaW8EsBjAB4RQnSvi9s+hoxuO2+/tkkpF0spt9qdg60pvkx3qsqksb2bB+0OEuv8YQs3TVytnJLdQxlbdAkLMxHVGwGBW7tfaxUm5OOlCS9y05GjMHToUH/1qO/SOryZmwGsB3C4ketHAFQCuBEAhBCzAPQAcKUQQlZ/XHXwBYQQtwohtgshyoQQHwkhUg75fpoQ4mUhxB4hRFAI8bMQ4pRDjpFCiD8LIZ4WQuwDsLym8NXH3iqEeEIIUSKEKBZC3F79vSuFEBuFEPuFEG8IIVwHXe5vQoj/mdZSfX0H7u9mAOkA7j/o/g6s/t5tQoj5Qojy6vvypRCi4KDrqfFndbhpGEKIUUKI5UKIkBBimxDiYSGE46DvX1V9HZ2EEN9XT5dZLYQYWdPP5vewLMcZj6beNKAg3V/UNNHuKDGvLGDg1klrxaBmfZTrOl1q2J2HiBo/BQK39xhrdUvrgJcmvKgYBh96fk/btm3RqlWrck3T/lpXt1FdxnoD+FJK+T8DKFLKcgAzAfSv/tJ4AKsB/Lv6cr0BfHXQRUYBGITo9IO7AAxFtHAfuD0dwDQAQwDcgegUhX0Apgkhmhxy83cAaArgcgB/+p27chuABACXAPgAwONCiMcAXFV92XsAXAbglt+5nkOdD6AcwOv4//d3UfX38gBMADAcwFgAKoCfhBDJ1d//vZ/VfwghzgDwcfV1DwfwHIDbq6//UB8AmFKdbR2Aj4QQecd4vwAAjt8/hBqZ/S6nevUdg1p/OO6jZZ6QwRGLI9lVEcJtn68TT5x/qqoIxXxxGU+wIaK6oUDgjp7XmZ1TCsWLz72ghEIhuyPFPE3TMHz4cL+maZcDCNThTWUA0AFsOcIxWwCcBQBSypVCCB+AfVLKwy1hFwEwQkppAIAQogOA0YgWRwAYA6AIQEcp5brqY6YBWINo4b3joOvaLaW8+Cjvxzop5XUHXd9FiBbYFlLKiuqvD0S0YP7zKK8TUsrFQggDwPZD76+U8j8rkwghVADfA9iLaNl95yh+Vgf7O4BZ1VNfAOAbIQQA/EMI8ZCUcvtBxz4lpXyj+nYXIrqT41AALx3t/TqAI8vxaYrHqU67/KS8sN1BGoLt+4O4+dO1ol9OL3FjlysswZP+iKiWKULBXSf90eyS2k68NOFFJRjkbLmjMXjw4JDD4ZgCYLrdWY7RzANFudpKAFlCiAPrlg4GsBDAJiGE46BpBrMB9Dzkug47CluD//ycpJQWgE0AFh4oytXWA8g9hus8IiFEr+rpECUADAB+REe32x7j9agAugOYeMi3Pka0z/Y+5OvfHfgPKWUJogX9uEaWWZbjlEdTx57TITPUJpMbcByN3ZUh3PjJaqV3k5Pwp65XsTATUa1RhIK7e15vdk5th5cmvKgEAnU5QNp45ObmomvXrkFd12+sh5srBhAC0OIIx7QAcLR7kO8/5PMwAAHgQFnOANAL0RHogz+uBtDskMvuOcrbrOl2D/c1F2qBEKI5oqVVALgOQB8AJyFaXI/1NjIAOPG/9/fA52mHfH3/IZ8f9/1iWY5fe52qcv2dg1r7HAqL39Eo8Ufwx49XKT2zuuOWbtewMBPRCVOFintOGm92TC7ASxNeVP1+v92RGgRVVTFy5Eif0+kcD6Ckrm+vehT4FwDnCiH+pzsJIZIQXTZtTi3dZCmABYgWy0M/zj80Xi3dZk2C+P8lHgAghEg9ysueBcADYLiU8lMp5c8AluB/i+3RKEb0BUPWIV/Prv639Diu86iwLMcxRYgPkt2OeaO65XAr7KNUETRx3UerlC7pXXB7j7FS+d/HTCKio+JQHPjryTdabb0txfPPTFB9Pu4bdbQGDRoU9nq9vwohPqzHm30G0akDfzjM9+4GkIT/PtHsREZopwMoALBVSrngkI8aV7yoI9sBJAohDp6accZhjjvc/XUjupTfwVNORuF/z5n73Z9V9YmVCxGdZ32wUdW38cuRLn8i+Ewf36RXc1x5Ydcm4fy0RrU0Y53yhaOFuW1yRzzQ61bJrbGJ6Fi5HS78o88dsoXWFC88+7wSDvMUkqPVqlUr9OzZ0+dyuUaj7kdV/0NKORnRk8Oer16qbbAQ4iwhxJsA/gLg/6SUiw66yGoA/YQQZwohegoh0o/h5t5BdD7xLCHENUKIgUKIC4QQjwoh6nsr728QPXnyDSHEGUKI6xB9cXCo1YiOvA+svr+JAGYguvrFm0KIQUKIPyF64uD+w1z2aH5W9wM4TQjxZvWxtwN4EMCrh5zcV6tYlmmbU1Wu/+uZbXyaymkFRytoWPjjR6tEsponH+/3F5ng5NxvIjo6SVoCnuj/f9Ib0OWLz73A5eGOgcfjwUUXXRTQNG0U6nBL6yMYj+jqEb0BfIHoyWatEJ1mcOjqEQ8BWAXgEwDzAZx3tDcipQwCOA3RlSMeQHTe7zMA2gCYd2J34dhIKYsBXIDoyXGTEV2p49LDHHoHAB+iJxzOB9CjehT8agCnAJhafbmLEF1m7mBH9bOSUn6H6KohPQF8iegSd0+gen3ruiKkrLcXZRS7hD9sTp61vuSM53/YXCuT+uOFAPDw0DZWk1RTueuHf6I4WGZ3JCKKYVnudDza926U7tgnP3z/Q45QHKPLL7/c36xZs1c0Tavv0VWKYxxZJgCQHk296vQ26VU9myX//tH0HxLAPVPXKWt2Qj4z8H40T8yxOxIRxahWyc3xzMD7sXX1Jhbl43DyySdbeXl5WzRNu8vuLBRfWJbpgDKXU73wjkGtA8ku7lVzrB6dvlHMWuuT/+p3DzqkFfz+BYgornTN7IBH+96FBT/Nw5dTvmRRPkZZWVkYPHhwUNf14YieDEZUb1iW6WCzHYp4/o5Brbl20XF45eetYuLiYvy995/Ru2l3u+MQUYwY0ryP/OvJN+K7r77F7Nmz7Y7T4DgcDowePdrncDhuRHTbYqJ6xTnLdCjNHzaXvjVvW9uvVuzli6njMLhtBq7vl4tP1/3b+mjtVP4MieKUAoE/dBptDmnWV/n0o4liw4YNdkdqkIYOHRrq1KnTN7qun496XP2C6AA+kdOhwh5NHXHNKc2CzVJ4rt/xmLa2GHd+sRbDW52Fu3tebzkVTmshijcehxsP97nDHNj0FPH6y6+xKB+nwsJCdO7cuVzX9asQg0VZCHGnEGKg3TlqgxDiXiHENCFEhRBCCiFaHuaYzdXfO9xH0yNc91tHuNwlBx33RyHEjuqPsYe5njeEEM/U2p0+ShxZpsMyLDlud0XwyRsm/uY1LP6OHA+PU8XTF7S1QigT9/3ylNgfqrA7EhHVgxxvFh469XZpVIatN159XeXScMcnMTERN9xwQ8Dlcg0G8LPdeQ5HCFEMYIKU8m92ZzlRQojtANYjuqzbMAD5UsrNhxzTDYB+yEVfBWBKKbse4bpbA8g85MvXI7qUXI6Ucp8QogOARYguAycQ3eClq5RyVfV19EB0zee2Usp6XXqKI8t0WA5FvJrm0X74Y58WIbuzNFT+SHTzkj1lHrxw2oNok9LS7khEVMe6ZLbH0wPuw/a1W/DKiy+zKB8nIQQuuugin6qq/0KMFuVGqLmUciCi5fewpJSLpZS/HvgAsBlAewBH3ElRSrnh4MtVX7YngGlSyn3Vh50OYLqU8jUp5auIbmhy+kFX8zSAv9V3UQZYlqlm0qOpowe2SS8eWJDOoeXjJAE89O1G8eniUvyjz504La8Xf5ZEjdR5+YPkvSffhOnfTMOUyVO44sUJ6NOnj5Gdnb3G6XQ+YHeWmgghNgNIB3D/QVMKBgohJgohZh7m+AeEEHuEEE4hRMvq4y8VQrwrhKgUQuwVQtx/mMsVCSG+qj6msvr6m9T2/ZFSWsdxsVGIdsmPjuVCQojOADrgv0u2huhOgQf4q78GIcRoAKmI7qBY71iW6UjK3U71nJv6twy0SOV22Cfis6W7cd9XG3Bdp8txQ+fLLQfnMRM1Grqq4c4e11mXtR2Ot19/CwsXLrQ7UoOWk5ODAQMGBKtP6DPtznME5yM6ZeF1RHf0643oNILXAAwQQuQfOFAIIQBcAeA9KWXkoOt4HNFSeCGiI7r3CyFuOOhyBQB+AuACcDmAqwB0BPBl9XXabTSAX6SUW47jckFEdwQ8YCGAwUKIzkKILgAGA1gghHADeBTALVJKW34fWJbp9yxzqsr4B85p63c7+etyIlbsrsLYD1eKTqk98OyA+2W2J8PuSER0gpon5uCF0x+UBc48PPfUs2LXrl12R2rQNE3D6NGj/U6n82oAW+3OcyRSysUADADbD5peUIHoFtXbEC22B5wGoCWANw+5mhVSyuuklN9KKf8P0eJ9jxDiwBPu/QB2AzhbSjlZSvkZgOEAugM4p47u2lERQrQA0AvHOKpc7WIA/67+eQEApJSzAXwGYCmAJQA+klL+AOBOAEuklNNOOPRxYvuh36Uq4u0E3fHJ7adz/eUTVRE0cN3Hq5R1ux2YMPAB9G7aze5IRHScBjU7VT7Z/6/YsmIjXnnxFSUU4ikeJ2r48OEBXdc/B/Cp3VmOV/V0hrcAXHHQ6O9VABZIKX875PDPD/l8EoAcAHnVnw+uPsYSQjiEEA4AmxCdK9yzpgwHjj3kQz2Bu3U4owFYAD45lgsJIU4B0AqHmecspbwW0fueK6W8TgiRB+AWALcJIVKEEJ8IIUqFEL9UnxBYL1iW6ai4ner1nXOStpzfuUksvyXWYDw5c7N4fs4O/LnbWHl9p8tMR60/hhFRXdFVDbd3H2te1+lSTP70c/z7q3/HwtvhDV6/fv2MgoKCzbquj7M7Sy14E0ALAKcJIRIBXADgjcMct7eGzw8sw5YB4C4AkUM+WgFodrgbrl7y7dDjIwBqe/3C0QBmSin3HMflKgF8dbhvSil3SCl3Vn/6KIBXpZTrAdwHwIHo/Z4M4J3jCX08OHGSjlbQo6nnjOmZu2zN3qrElbur7M7T4M1cX4JVe6rEYyN6oSij0Hrg12eUvYESu2MR0RHkJTTB/b1ulrrhxIvPviAqKyvtjtQotGvXDv369avQNG0IonN4GzQp5WYhxDRER5TzER2cPNyKEVk1fH5gPk8poiPLrx3mssU13PxOACcd5uu19taHEKIQQFcA1x7j5RQAFwGYLKUM/M6xvRFdDaOw+kunAbhXSukTQjwP4J9CiKSDp3LUFY4s07HY7HKqF997Zlt/ipuvs2rD7soQrnrvN7WkwiMnnPZ39Gnaw+5IRFSDwc37yKcH3Ie9G3bjuaefVVmUa0d2djZGjhzp1zTtLAA77M5zjMKInnx3OK8jOqI8HtFyuP8wx5x/yOcjES3K26s/nw6gCMBCKeWCQz42H+5GpZThwxy7QEq5/Nju2hFdguh9n3SMl+sPIBe/s9Rc9fSVZwD89ZAy7Kn+13uMt3tCuCkJHbOQYT28tSxw8+2TV3LDklp0epsM/LFvjlyy7zf57NK3FV+kwQ+uEDUKKXoSbu/+B6sguaX44tPJYt26dXZHajS8Xi+uv/56v9frvUYI8bHdeY6VEGIGoptt3ASgCsAaKWVl9fd0REd50wCcKaX87qDLtUR07vFOAFMRPbGtP4B7ANwspXyu+ri2AOYhutb0G4iOJucCGALgLSnlrFq8LwOq70sPAHcjWvL3AVgppVx5yLGrqu/riBquywDwdynl3w/5+kuIrvzRREpZ4yLkQogrAdwMoOeBJe2EEE8j+jP6C6Iri7SXUnY/9nt67DiyTMdMdyj35ia75twyMP+Ib6HQsZmxrhhXvbdCZDjbyFcH/QPdMjvaHYko7p3atAdeHvQwEn0uPPXYkyzKtcjhcGDMmDE+XdefbohFudodAHyIzr+dj2jRBABIKUMAvkZ0ZYyaVnK4E0ASomX5OgAPIrpz3YHrWIvoihN+AK9UX98DiE6pWF+7dwUPAJiIaFEGgBeqPx918EFCiK4A2uHIq2CoOKRjVp+ceAGAT3+nKCcAeATRpeIOXvv5AQBbqjMVIlqY6wVHlul4ef1hc+HHi3e2/nTJLs7JqGXndMjC1b2ayh93zpMvL/9ACZlhuyMRxRWvw42bul5ldc/sKL6Z+rVYtmyZ3ZEanQsvvDDQpk2bGbquD0N0VYVGpbocbgHwhpTy3kO+1xLRkeXzpJRTbYhHx4BlmU5EXjBiLn18xoa0XzfvtztLo5PscuDBoa2tFI9U/rngRawqre1BBCI6nK6ZHXBHj3GoKCm33n/nPSUYDNodqdHp27ev0a9fv/W6rvdAIzih72BCCA1AFwCXArgeQIGUcvshx7QEy3KDwbJMJ+qkYMScdccXqzwbSxrV413MuKBLE1zSMxvfbp5tvbv6c44yE9URj8ONPxRdbPXLOUmZNW0m5s6da3ekRqmwsBAXXHBBqaZpXfD/T2RrNA4qwnsB3CmlfPsIx7AsNwAsy3TCLClHVQSNN2+c+JunLBD5/QvQMcvwOvHAOa2tZA+UZxa/gQV7a/OkZiLqm9MTN3S5ApVlFeaH733AlS7qSHZ2Nq699lq/pmmnIXriGlHMY1mmWhE0zAd3lYdu/fPnK7xhk79TdWVYUTYuPzlb/layxpqw9B21NLjf7khEDVq2JwO3dLvGbJ3UXJk9fZbgaHLdOWjliz8IIY64dBhRLGFZptoi/GFz0uLt5Wc+8v16t91hGrMETcHfzm5j5me41LdXTrK+2jRdscC/Y6JjoQoVFxScZY1qe66yfcs285OPP1HDYU5xqiuqquLaa6/1ZWRkTNA07e7fvwRR7GBZptrk9ofNuVNX7Gn39rztTrvDNHYDWqfhj/1yrf2hMjyx6FVlQ/lWuyMRNQjtUlvj9h5jpW455OcTP1e2buXfTl0bOXJksLCwcIau6+ehEa58QY0byzLVtsxAxFz85txtTb5asVe1O0xjpwjgT/1byn4FKWLOjrnm2ys/U8vDnGtJdDjprlSMLbrY6pHVSZn706+YNWuW3ZHiQp8+fYz+/ftv1HW9O6JrEhM1KCzLVBdaBSPmgidmbkz5eVOZsDtMPMhK1PCXwflm8zSX+vHaqdbkDd8pEavGNd+J4opL1TGqzTnW8NZDlC2bt8hJn04SXA6ufnTs2FEOHz58f/XKF9vszkN0PFiWqa50C0bMH+7/eq33t10c6awv3fKScPOAZqbTaSqvLP9QzNnBk80pfikQGNS8j7ym4yjhr/SZkz75TN2zZ4/dseJG9RJxFZqm9QXAJXyowWJZpro02B82p9z5xUr3plLujF2fRnZuglE9MmVxoEQ+v/QdZXXZBrsjEdWrzhntcEOXy61E1YtpX3+vcAe++tWqVSuMHj26qnqJuAV25yE6ESzLVKcsKS/yhcy3b/l8hXt3RcjuOHFFFcD1fVvI09qmiKXFq8w3V0xUt1fttjsWUZ3KT8rDtR0vNgtTW6nLFi2V33zzDZ/n6lnz5s0xZswYn6ZpZwL4ye48RCeKDyJU5yKmdX1F0PjXzZNWeMr83LSkvnk1FXecnm91zk1Q5u9ZZr6zapK608e3oqlxaZGYi6s7XmQWpbdVN63bKCdPnixCIb5Ar2+5ubm44oor/LquDwMw3e48RLWBZZnqRcgw7yvxRe66ddIKT1XYtDtOXMrwOnHHoFZmmyy3Onf3EuPdVZ87dvn22h2L6IQ0T8zBVR0uNDtntFO3bNxsTf58shIIcNqXHbKzs3HNNdcEdF0fBYBbOFOjwbJM9UUEIuazeypCV98xZZXXz8Jsm2YpLtwysKWRn+Fy/LprsfnOqknqHn+x3bGIjkleQlNc2WGk2T2zSN2+dbv1+aRJSlVVld2x4lZGRgauvfbagMvlulIIMdHuPES1iWWZ6pMIRMyXd5UHL71zyipvIMJ16e2UnajhTwNaWO2beJWfdy60Pl47VeGcZop1+UnNcEnhMLNHVpG6fKfPapWqy7k/zlbmzp3LZSptkpqairFjxwZ0XR+vqupbduchqm0sy1TfRCBivrZjf/Diu79kYY4FqR4HbujXAt3yErGmbIP10ZovleUla+yORfRfemQV4ZLCYWbLpDx1yfYq+dycLWJ/wMCAgjTcdGounnjiCRgG1xavb0lJSRg3bpzf5XLd6XA4nrc7D1FdYFkmOyiBiPnG9v3BC++essobNFiYY4GuClzTqxkGtk2W+0Pl+HDNFPHjzgUwJafMkD0cigOn5fXC6MLzLK/DK37eWIWXf9oiDn3MeOeSInPuj7OUefPmcXS5Hnm9XowbN87v8XgecDqdj9mdh6iusCyTXZRAxHx7a1ng/L98udobYmGOGQLAiM7ZGNElw9JUqUxc92/57ZY5wm/wpCmqH4lOL87NP90aUXCGEjGk+f2qcvXdBdth1fB0NbAgDTf0zsGTTz7J0eV64na7MXbsWH9iYuKTTqfzXrvzENUllmWyk+IPm+9vLQsMu2fqag8Lc+zp1SIFV/ZqYmUl6MqcHfPMqZtmqBvLt9odixqpNiktMazVEOvUnO7KzvKg+d783eqvm/cf1WXfubTI/HXOTGX+/PkcXa5juq7j2muv9aWkpLymadqtAFgkqFFjWSa7qYGw+eGmUv+5f/1qDQtzjGqR5sZ1pzaz2ma7leJAqfnFhu+U2TvmiYARtDsaNXBuhwv9c0/G+QVnIlVLkQu3Vok3527H3qrwMV3PaQXpGN+7KZ544gmYJqcO1RWn04mrr77an56e/oGu6+PAokxxgGWZYoHqD5sfbyrxn33vv1mYY5nLITCqey5Oa5NkJrs19ccdC8ypm6ar6/ZvtjsaNTCFqa0wNP9069ScHsreyqA1eWmJ8u3qfSfUvN69tMj8efYMZcGCBRxdrgMulwtjxozxZ2ZmTtF1/TIAfLCmuMCyTLHC4Q+b7+6qCJ53z5ervdy4JPY1T3XhypPzZKccD8rD5WLqphlyzo55Yn+owu5oFKMy3ekYkHuyPDt/ILwOLxZt9Ym35m3HnspjG0WuyeC2GRh3chM8+SRHl2tbQkICrr76al9CQsL7uq5fDxZliiMsyxRLlEDEfK7UF7nyrimrvGUBbo3dEAgAg9pm4LxOabJZqldsLN8iv948W/yyaxE4TYNS9CT0zTlJntWiv9U0IUvdsT9oTl66T52+rqRObu+9S4vMH2dNVxYuXMjR5VqSlpaGq6++2u9yuR5zOp1/B6deUJxhWaZYI4IR84GqkHnbHV+s9BzrvEWyl+5QMLJzE5zWNsnKTHArv5WstaZt/VGZt3spgmbI7nhUT7wON07N6YEzW/S3WiU3V8oCIeOnDZWOjxbthK+O3zU6ozADfzgpG08++SRHl2tB06ZNccUVVwQ0TbtVVdWX7c5DZAeWZYpJEdO62R82H7lryirPtv0cnWyIkl0OXNIjF73yPWayy6UuK15tztkxT124ZznKw5V2x6NaluZKwUnZnTEg9xSrXVprpdgXNH/ZWKVOXLILlaH6Xc7tvcuKzB9ncnT5ROXn52P06NF+XdfHAPjc7jxEdmFZpphlWvLKYMR88Z6pa9zri312x6ET0CRJw6U98mTnXJeZ7NIdu3x7rdnb54p5e5aKzRXb7Y5Hx0FAoCClBU5p0lX2yz0ZGe40sbPcb/26qUqZvGw37Dzv4Mx2mbimZxae4ujycevQoYMcMWKET9O0oQBm252HyE4syxTrhgci5gd/+3qt57ddHI1sDNxOBWd3yEK/VslWboomTGmKX3cvtn7euVBZXrIGIZNTb2KVx+FGp4xC9M3paZ7UpIsqLcjNJWF8v6ZUzFpfArOmXUNs8P5lReacGdOURYsWcXT5GJ100knmkCFDKjRNOx3AErvzENmNZZkagtODEXPKY9M3eOdu2W93Fqpl7bO9GNoxGx2a6laq263sqNot5+9ZhmXFq8Wq0vWc62wjj8ONDult0DWjvdUju5Ns4s1Uy4NBc+XOoPrFb3uwek/svuNzdvtMXNU9Uz755JPCsrhww9E67bTTIr179y7WNK0vgI125yGKBSzL1FCcFIyY3709b3vilN/2qHaHobqhOxT0b52Gfq1T0TLdaSW5XMqOqj3Wgj1LsWzfamUly3Od8jjc6JBWgK5ZHayeWZ1EtjdTVIaC5s79pliwtUL5ZtVeVIYazrSGDy4rsmZN/14sXryYo8u/QwiBoUOHhoqKijbruj4AwB67MxHFCpZlakjyAxFz5vS1xU1e/mmLHkPv+FId8WoqzmyXid75yVZOigMJmq4UB/Zba8o2WCtL1znW7d+MTeXbELHq9wSyxkBXNeQnNUPb1Hx0TG9rFabmK8l6Ekr9AWtTcUT5dfN+zF5fgrDZcP/Qzu2QhSu6ZXB0+XeoqoqLLrookJ+fv1TX9TMBcLF0ooOwLFNDk+oPm/9etaey88Pfreduf3HG41TQOz8V3Zslo1W6ZqZ4HMLj1JS9/lJrddkGuap0nbqhfCu2V+6G3wjYHTdmJDi9yEtoglbJzdExvY3ZLrW1ku5OEVXhkLW30sDq3QHl501lWLmnKqbmHdeGDy4rsmZO+04sWbKEo8uHoes6xowZ48vKypql6/oFAPjWDdEhWJapIdL8YfPtYl/4vHumrvaW+bl5STxL0FWc2jIV3fOS0TzdaaZ4VOF16krQCGOXb6+5qWKb3FSxzbG9cje2V+1CcaAMshHuqSAgkOlOQ15iUzRLaIr85GZmflIzNPVmqk7FAb8RtioClrVhX8gxb8t+/LK5rEGPGh+toR2zMKZLunzqqac4unwIr9eLq666ypeUlPSxruvjADScOTZE9YhlmRoqETKs+wIR8857pq72bCnlKCL9fwJAYZYXXXKTUZjllbkpTiPBJRSXw6GqioqyYLksDpTK3f5iucu3VykOlIp9gVIUB8pQHCiNyXnRbocLGe5UZLrSkOFOQ6Y7TTZNyDabeDJFhjtVSdYThWGZ8IcjZnnAws79EXVdsR/Ld5Zj3T4/GtmA8TH5YEwna8Z334ilS5dydLlaVlYWLr/8cr/L5XrK6XTeC+7KR1QjlmVq0CwpLwkZ1msPf7fOs3g7p9nR70txOVCQ5UWLVDdykl3IStCQ4oGVoCtwOx3C7dSFKU34IgH4IwFZGfFZleEqWR6qxP5QhVoZqRJVYT+qIn6ErQgMy4BpmYhYBkxpwrAMRCyz+r9NKEJAFQoUoUIVChyKA5rqhKY44VQc0FUNXqcHCZoHSVqiTNGTZLKWKBO1BJGoeYXH4RYepwuqUBEywzJkmFYgImW5Xyq7K8LK9v0BbCzxY83eKlQEOTB4OMOLsjG6U5p8+mmOLgNAUVGRHDZsWMDpdF4nhHjP7jxEsY5lmRqDvsGI+dUbc7clfLVir2J3GGr4EnUHcpJ15CTpyE5yITNBQ7rHKT2aQzodUjpVCacKqIqAIiAUIaAIIPqvECL6LxQholM+JCCrJ39YUkrTkjAtKQ3LQsSUIhiRSiAM+MMSFSED5QED+wMRlPojKKkKY29VCCW+CIf+TsCHYzpZ07/7Rlm6dKndUWyjKArOOOOMUPfu3fdrmnYWuIYy0VFhWabGoiAQNr/9dUtZ02dmb3JH4mAuJhEdvRGdsnFxUap8+umn43J02ev1YvTo0b6srKxFuq6PAFBqdyaihoKjcNRYrHdrapdTWqROe2ZkkS8zQbM7DxHFkMnL9wCqUxYVFdkdpd7l5ORg/Pjx/iZNmjyv6/ppYFEmOiYsy9SYVHk0dXhOsv7ghAuLAp1zEu3OQ0QxZOKyfcqgQYOkEPFznl+3bt2sq666yuf1esc4nc67wBUviI4ZyzI1NtKpKo8m6I7z7j+rbcXIzk34xEBEAIBJy3ZDcWpxMbqsqiqGDRsWPOuss7ZrmtYTwOd2ZyJqqFiWqbGa7nKqnS/pkbvuniEFAd3BX3Uiio4uDx48uFGPLicmJmLs2LG+jh07ztJ1vROA1XZnImrI2CCoMdvi0dTu3fKSv5hwYZGvSZJudx4istlnS6Ojyx07drQ7Sp1o0aIFxo8fH0hPT39U1/VzwK2riU4YyzI1dgGPpl6amaDdPeGCokD/1mlcJoMozn22vHGOLp9yyinmZZddVul2u0c4nc4HwY1GiGoFl46jeNI9EDGn/LypLP35Hza7Qkb8LR9FRFEfX97Z+vqrL5UVK1bYHeWEOZ1OjBgxIlBQULBd1/UzAWyyOxNRY8KRZYoni9xOtX3vlqlTXhrVyd8q3WN3HiKyyaTf9ilDhgxp8KPLWVlZuO6663wFBQX/1nW9K1iUiWodyzLFm0qPpl6ckaCNe3x4e995RdkcXiaKQx8v3gWH5pIdOnSwO8pxEUKgT58+5tixY/2pqal/1nX9IgB+u3MRNUachkHxrMAfNqes2lPZ4vHpGz2VIcPuPERUj0Z3z8HQAq989tlnG9RzYWpqKi666CJfenr6muqSvNHuTESNGUeWKZ6t92hq1w5NEl97+eJO/qKm3MSEKJ58tGgnNJdbtm/f3u4oR61Hjx7W9ddfH8jKyvq7rusng0WZqM41qFfTRHXorGDE/PDrVfs878zbpoVN/l0QxYNLe+Tg7FYe+dxzz8X082FiYiJGjhzpz8nJ2abr+kgAK+3ORBQvOLJMFPWNy6m2PbNd5ncvXdzZV5jltTsPEdWDDxbuhObyyHbt2tkdpUZFRUXyxhtvDOTl5T1dvckIizJRPYrpV9JENrkoGDFf+2rlXte787drEY4yEzVqY3rm4sx8d8yNLns8HgwbNsyfn5+/T9f1CwEssDsTUTziyDLR/5rocqptzmqfOf2lUZ19bTI5ykzUmL23YAd0tweFhYV2R/mPtm3b4qabbgq0atXqTV3X24NFmcg2MfUqmijGCAAXhwzzlS9/26O/O3+HZlj8eyFqjC4/KRdDWrjkc889Z+vCy7qu4+yzzw526NChQtO0UQBm25mHiFiWiY5Gti9svF0RNPr+8/sN3vXFPrvzEFEd+OSKznLK5M/FmjVrbLn9li1b4sILL/Q7nc5Juq6PB1BpSxAi+i8sy0RHR1hSjo6Y1svfrynW3p63XfeHTbszEVEtuvLkXJzeTJcTJkyo19FlXdcxaNCgcNeuXX2apl0O4Kv6vH0iOjKWZaJjk+EPm0+bljz/hR83e+ZsKLU7DxHVoolXdJZf1NPoshACXbp0kWeeeWZQUZTJuq7fBKCkzm+YiI4JyzLR8TnVHzbf2VTib/L07E3eneVBu/MQUS24+pQ8DMjT5PN1PLqck5ODYcOGVaWkpGx2uVzXAJhfl7dHRMePZZno+DkipnWzJeXfP1+2x/nxoh1ObmZC1LApAD6+srOcPGmSWLt2ba1fv9frxRlnnBFs37590Ol03iKEeBeAVes3RES1hmWZ6MTl+cLGy8GINeCZ2Zu8C7eV252HiE7ANb2aoV+Ow3rh+edrbXlVRVFwyimnWAMHDgwJIV7TNO1eAHywIGoAWJaJas9ZgYj5xrKdFckv/7TFs6cybHceIjoOB0aXP//sM7Fu3boTvr5WrVrhvPPO87nd7iUul+sPAFaf8JUSUb1hWSaqXe6wYd0lIe/4asVexwcLd2iBCN9hJWpo/tCrGfo0Va0XXnjhuEeXU1JScM455/hbtmxZqWnadQCmAOCTLlEDw7JMVDdy/GHzX5aUI96au8317ep9gvuZEDUcCoBPruwsP/v0U7F+/fpjuqzT6UTfvn2N3r17h4UQjzmdzkcB8CxgogaKZZmobnX3hY2XK4NGu5d+2pIwfyunKBI1FGN7N0fvbGG9+OKLRz263KFDB5x77rl+VVWnu1yuGwBsq8OIRFQPWJaJ6p4AMNQfNp/fWhZIffHHzQnri/12ZyKi36EK4OMrushPJ34iNmzYcMRjmzRpgnPPPdeXlZW1R9f1a8BtqokaDZZlovrjMC15bcS0/rlgW7n29txtnp0VIbszEdERXHdqc5ycKayXXjr86HJWVhYGDx7sa9myZURV1b+qqvoyAKOeYxJRHWJZJqp/CWHDukNC3vbzpjLlvQU73LtZmolikkMBPrr8f0eX09PTMXjwYH9BQYGpKMpDqqpOAMC3jIgaIZZlIvukhAzrdgC3/LixVH1/wXYXl5sjij1/7NMCPTOk9fJLLympqak4/fTTA+3atTOFEI85HI6nAVTanZGI6g7LMpH9UkOGdSeAP83ZUKK8v2CHa18VSzNRrDgwurx921ajZcuWYQBPO53Ox8FNRYjiAssyUexID0bMu4UQ42euK1Y+WLjDVeKL2J2JKK41TdJxaY/cQN9WaUJI81On03kzgFK7cxFR/WFZJoo9mcGIeY8Q4rrZ60vExCW7XDvLuUQrUX3KS3Hhsp65gVNapFoAntEdyhNgSSaKSyzLRLErK2xYt0jgpuU7K8RHi3Z6V+2psjsTUaPWsUkCRnXL8XXKSZRCiMc1VXkGnG5BFNdYlolin9ey5DUh0/rrzvKg54OFOxLmbt7PPXOJaokigL6t0jC6e05lZoJeqTuUh1VFvA3AZ3c2IrIfyzJRw6ECGOkLGQ8EIlbzjxbt8ExfWyzCJv+GiY6HR1NxVrtM68KuTYOqItYk6I6/AZgKwLI5GhHFEJZlooZHAOhfFTL+JoBTJi/f4/x65V5HWYAnAxIdjcwEDSM7Nwmf0S7TMi35rVd3PARggd25iCg2sSwTNWwd/GHzbociLly0vdz6Yvlu77KdXPKV6FACQOfcJAwvyvZ1zUsWUspXXU71CQDb7M5GRLGNZZmocUiWUo4JRKw7fGEzffKy3Z5pa/cpVSHT7lxEtkp2OTCkMNMa1ik74HIqez1O9UkhxDsAKuzORkQNA8syUeMiAPTxhYw/O1XlnJ83lVlTftvtXrOX5ylR/DgwijysKNvXPS9ZNSw52aOpzwCYC/DcWCI6NizLRI1XpmHJayKGdWuJP+z5YvmehDkbSgRHm6mxSnFHR5HPK/rPKPITQoj3wKXfiOgEsCwTNX4KgCFVIeMmTVUGL9tZEfl29b6EeVv2w7D4908Nm1MVOKl5Cs5sl1nVOSfJEbGsz72a4xkA88BRZCKqBSzLRPElGcAFVSFjvEMRHX/YUIrv1uxzrdpdxVZBDYYigM45SRhcmBHo3TJViVhyWaLueAnAZ+AoMhHVMpZlovjV3LDkmJBhXhc2ZPp3q/fp09cWO3Zwa22KUQUZXgwqTA+f3ibDBLDNo6mvKEJ8CGCn3dmIqPFiWSYiAaBrMGJeC2BMiS8sZq4r8f68qUzdUhawOxvFuWYpLvRvnW6c0S4z6NHUKociXtccynsAVtudjYjiA8syER3MAaBPMGJeLIGL/GHTNXt9if7jxlLn2r0+TtWgOqcIoH12Ak7NT4v0a50W8mpqWAKfuJ3qmwDmg/OQiaiesSwTUU0EgO5h07owYlqXSYmMHzeWih82lLqW76qEyZMDqZboDgXd8pLQt1Va4JQWqcKScqfuUD5yqsokAIvAgkxENmJZJqKj1daw5AXBiHmFQxH5i7aXG3M37/cu3l6OEj+32qZjk+px4qRmyehfkF7ZsUmiHjKsZQm6+p4Q4gsAm+3OR0R0AMsyER2PPABnVoaMkbqqDCgLRKy5W8r0BVvLtd92VSJkWHbnoxjjdirolJOE7nnJ4ZNbpIRS3U41bFqzE3THBwD+DaDU7oxERIfDskxEJ0oF0N2w5FmBsDnS5VQ6bCj2B3/ZXJaweHu5srHYz/fQ45BDESjM8qJbXrJ5SstUX7MUlysQsZZ5NfVzVRHfIzq9gjvkEFHMY1kmotqWCGBAIGIOlRLnCYG0tXt94SXbyxNX7KkS6/ZWIWzycaex0VSBgkwv2mcnyJ7NUyrbZSW4wqa1xakqU3SH8g2AnwBweRUianBYlomorjUF0CdkmAPDhhzkdiqtd5QH/Ut2VHh+21XpXLW7CmUBznluaLISNLTPTkDHpomhTjlJwZwk3ROIWBudqpjlcqrTAMwEUGJ3TiKiE8WyTET1zQPgJNOSfX1h8yyXQ+nuCxvWb7uqxKo9ld71xX5sLPYhEOG851jh1VS0TPegXZZXdslJqirMTnA4FBGOmHJ+gq5+J4T4BcBCcOSYiBohlmUispsCoD2AXoGI2dsw5alup1JQHjSC6/f5sHpvVcKmkoDYVOJHsS9sd9ZGTRFATrILLdM8aJXuMQuzvb78NI/q0VRHMDpqPMflVGcD+AXAFnBJNyKKAyzLRBSLHADaAugeMqyeIcPspalKOwCu7fuDwU0lfm1rWcC9ozyIneVB7KoIweC6z0dNEUB2oo6cZBfyUlxok+n1t8n0RrITdU/ElKWGJZd7NfVnVRFLACwDsAkAh/qJKC6xLBNRQ5IFoBOAwmDELAqbVieHohToDiWjImgEd1UEzc0lftfW/UF9Z3kQuytCKPGFEYzDpex0h4LsRB1NknQ0iRbjcItUdzAnxSVS3U5PyLDKDMva5FSUpW5NnY9oKf4NQJXN0YmIYgrLMhE1Bk4ALREdjW7jD5udDMvqpCqiue5Q00xLyopgJFTsi1h7K0OOPZUhd7EvrJT4wijxRVDqC6MyZDSIVTp0h4I0jxNpHidSPU6kejSke51mVoIezEzQjDSPUyS5nJrmUByhiLnXlHKzQ1FWeTR1JYCNANYB2AAgaO89ISJqGFiWiaixEwBSAOQe+JBS5vojVmvDsloqEHlOVWQ6VcUrAYQiZsgfMU1fyLQqQwYqgoZSETScFUFDqwoZSiBiwrAkIqaEYVkwTImIJWFYEoZp/ed7piWhCEBVBFRFQBECjur/PvC5qkTXI9YdKjxOFR5NRaKuGgkuRzhRd5gJmmp5dQc8mircTkV1OVVNFUJER4XlXkDudCrKZrembgGw65CPfeDUCSKiE8ayTEQUJQC4AKQiWq4P/TclGDGzTEumWBIuCbgAqUsJHYAGQBMCGiA0IaAJwCkBAxKmBAxAGgAMKREBYBz4EAIRAVGlKqJEcyjFDkWUAagAUH6Yfw988IGbiKiesCwTEREREdVAsTsAEREREVGsYlkmIiIiIqoByzIRERERUQ1YlomIiIiIasCyTERERERUA5ZlIiIiIqIasCwTEREREdWAZZmIiIiIqAYsy0RERERENWBZJiIiIiKqAcsyEREREVENWJaJiIiIiGrAskxEREREVAOWZSIiIiKiGrAsExERERHVgGWZiIiIiKgGLMtERERERDVgWSYiIiIiqgHLMhERERFRDViWiYiIiIhqwLJMRERERFQDlmUiIiIiohqwLBMRERER1YBlmYiIiIioBizLREREREQ1YFkmIiIiIqoByzIRERERUQ1YlomIiIiIasCyTERERERUA5ZlIiIiIqIasCwTEREREdWAZZmIiIiIqAYsy0RERERENWBZJiIiIiKqAcsyEREREVENWJaJiIiIiGrAskxEREREVAOWZSIiIiKiGrAsExERERHVgGWZiIiIiKgGLMtERERERDVgWSYiIiIiqgHLMhERERFRDViWiYiIiIhqwLJMRERERFQDlmUiIiIiohqwLBMRERER1YBlmYiIiIioBizLREREREQ1YFkmIiIiIqoByzIRERERUQ1YlomIiIiIasCyTERERERUA5ZlIiIiIqIasCwTEREREdWAZZmIiIiIqAYsy0RERERENWBZJiIiIiKqAcsyEREREVENWJaJiIiIiGrAskxEREREVAOWZSIiIiKiGrAsExERERHVgGWZiIiIiKgGLMtERERERDVgWSYiIiIiqgHLMhERERFRDViWiYiIiIhqwLJMRERERFQDlmUiIiIiohqwLBMRERER1YBlmYiIiIioBizLREREREQ1YFkmIiIiIqoByzIRERERUQ1YlomIiIiIasCyTERERERUA5ZlIiIiIqIasCwTEREREdXg/wEzK2K32rOxnQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make data: I have 3 groups and 7 subgroups\n",
    "group_names=['SNP – 82.3%', 'Other mutation \\n type – 17.7%'] #\n",
    "#group_names=['', ''] #\n",
    "snp_unrecorded=0\n",
    "\n",
    "mut_types= impact_data['Variant_Type'].value_counts()\n",
    "\n",
    "group_size = [mut_types[0], sum(mut_types[0:])-mut_types[0]]\n",
    "\n",
    "#subgroup_names=['editable', 'non-editable', 'non-editable','editable']\n",
    "\n",
    "    \n",
    "rt_len = 20-1\n",
    "sum1 = ONP1[rt_len]+DNP1[rt_len]+INS1[rt_len]+ DEL1[rt_len] + TNP1[rt_len]\n",
    "\n",
    "subgroup_size = [(mut_types[0]-SNP1[rt_len]), SNP1[rt_len]+sum1, (sum(mut_types[1:])-sum1)]\n",
    "\n",
    "perc= np.round(100*np.asarray(subgroup_size)/sum(subgroup_size),1)\n",
    "subgroup_names=['', str(perc[1])+'%','']\n",
    "#subgroup_names=['', '','']\n",
    "    \n",
    "# Create colors\n",
    "a, b, c, d=[plt.cm.Blues, plt.cm.Reds, plt.cm.Greens, plt.cm.Purples]\n",
    " \n",
    "# First Ring (outside)\n",
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "ax.axis('equal')\n",
    "mypie, _ = ax.pie(group_size, radius=1.3, labels=group_names, colors=[a(0.6), 'tab:gray', c(0.6)], textprops={'fontsize': 15})\n",
    "plt.setp( mypie, width=0.3, edgecolor='white')\n",
    " \n",
    "# Second Ring (Inside)\n",
    "mypie2, _ = ax.pie(subgroup_size, radius=1.3-0.3, labels=subgroup_names, labeldistance=0.7, colors=[b(0.6), c(0.6),b(0.6),c(0.6), b(0.6)], textprops={'fontsize': 15})\n",
    "plt.setp( mypie2, width=0.4, edgecolor='white')\n",
    "plt.margins(0,0)\n",
    "\n",
    "ax.set_title('RT Template Length = ' + str(rt_len+1), fontsize=20)\n",
    "# show it\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "fig.savefig(str(rt_len+1) + '_NGG.png', dpi=250)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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